Real-time prediction of tissue fixation time

Abstract

The present disclosure provides systems (200) and methods that facilitate prediction of an estimated time for one or more fluids to be optimally diffused into a biological specimen, such as a tissue sample derived from a human subject. In some embodiments, the present disclosure provides systems (200) and methods that facilitate prediction of an estimated time until a biological specimen is optimally fixed by one or more fixatives. In other embodiments, the prediction of the future time at which the biological specimen will be optimally fixed is based on time-of-flight data acquired at a particular time point during fixation of the biological specimen that is deemed sufficiently accurate to predict the time at which the biological specimen will be optimally diffused by the fixative.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of the filing date of U.S. Provisional Patent Application No. 63 / 187,976, filed May 13, 2021, the disclosure of which is incorporated herein by reference in its entirety. [Background technology]

[0002] Background to the disclosure Appropriate medical diagnosis and patient safety require proper fixation of tissue samples before staining. Therefore, guidelines for proper fixation of tissue samples have been established by oncologists and pathologists. For example, according to the American Society of Clinical Oncology (ASCO), the current guideline for the duration of fixation in neutral buffered formalin solution for HER2 immunohistochemistry analysis is at least 6 hours, preferably longer, up to 72 hours.

[0003] Several effects are observed in tissues that are exposed or overexposed to formalin. If tissue samples are not treated with formalin for a sufficiently long period of time, the tissue morphology is typically very poor when the tissue is subjected to standard tissue processing. For example, in improperly fixed tissue, the tissue does not have an opportunity to form a proper crosslinking lattice, so subsequent exposure to ethanol shrinks cellular structures and condenses nuclei. When fixed tissue is stained with hematoxylin and eosin (H&E), for example, many white spaces are observed between cells and tissue structures, nuclei are condensed, cytoplasm is lost, and the sample appears pink and unbalanced by hematoxylin staining. Tissues exposed for too long to fixatives such as formalin typically do not perform well in subsequent immunohistochemistry processes, likely due to denaturation and degradation of nucleic acids and / or proteins. As a result, optimal antigen retrieval conditions for these tissues do not function properly, and therefore, the tissue sample appears stained.

[0004] Monitoring the diffusion of fixative through a tissue sample is useful for determining whether fixative has permeated the entire tissue sample, thereby minimizing or limiting under- or over-fixed tissue. For example, if the tissue is "over-fixed," it may be difficult for processing solutions to diffuse through the tissue due to an overly extensive network of bridging molecules that restricts pathways for diffusion. On the other hand, if the tissue is under-fixed, it may degrade, for example, by autocatalytic breakdown, resulting in loss of tissue and cell morphology, as well as loss of diagnostically important proteins and nucleic acid markers. Algorithms have been utilized to determine how much fixative diffusion is required to ensure adequate fixation and therefore proper and ideal staining. For example, U.S. Pat. No. 10,620,037 (the disclosure of which is incorporated herein by reference in its entirety) describes a system and computer-implemented method for calculating the sample's diffusion rate constant (i.e., diffusion coefficient) using acoustic time-of-flight (TOF)-based information correlated with a diffusion model to reconstruct the diffusion rate coefficient of a biological specimen. The '037 patent further describes a method for determining the true diffusivity constant of a biological specimen immersed in a fluid, comprising simulating the spatial dependence of diffusion into the sample over multiple time points; generating a model TOF for each of multiple candidate diffusivity constants; and comparing the model TOF with the experimental TOF to obtain an error function, wherein the minimum of the error function yields the true diffusivity constant.

[0005] As another example, WO 2016 / 097164 (the disclosure of which is incorporated herein by reference in its entirety) describes a computer-implemented method for accurately calculating the TOF of an acoustic signal passing through a material (e.g., a biological specimen), including obtaining an error function of a frequency sweep of the acoustic signal and generating an envelope of the error function, where the time of flight is based on a minimum of the error function. For example, the '164 publication describes one of three methods: (1) calculating the TOF of the acoustic wave by calculating the envelope of the error function, which allows for a more accurate determination of the minimum of the error function; (2) fitting ultrasonic wave frequency sweep data to multiple simulated TOF frequency sweeps, where the TOF is calculated directly from the best fit; and / or (3) performing a linear regression analysis on individual linear portions of the ultrasonic wave frequency sweep, which allows for the identification of anomalous sections of the frequency sweep that may represent errors in the TOF calculation.

[0006] The diffusion rate of fixative through a tissue specimen can vary significantly throughout an experiment because data points are collected continuously. This variability is due in part to noise inherent in TOF systems, but also to inherited variability in the tissue specimen (e.g., sporadic diffusion, tissue deformation, etc.), which masks the true diffusion signal of the tissue specimen. It would be desirable to have a model that can predict in real time whether the TOF signal is sufficiently valid, and such TOF data could be used to determine the time to fixation of the tissue specimen. Summary of the Invention

[0007] A brief summary of the disclosure Variations in fixation of a biological specimen, such as a tissue or cytological sample, can affect downstream biomarker labeling and / or staining processes, resulting in indeterminate results and / or misdiagnosis. Therefore, it would be advantageous to have a system and / or method that facilitates determining whether a biological specimen has been properly fixed so that downstream staining and / or labeling processes can be performed on a properly fixed specimen.

[0008] Applicant has developed systems and methods that facilitate determining whether a measured TOF signal accurately reflects the actual diffusion rate of a fluid, reagent, or solution (hereinafter collectively referred to as "fluid") through a biological specimen. Accordingly, the present disclosure provides systems and methods that facilitate predicting the estimated time for a fluid to optimally diffuse through a biological specimen, e.g., the time it takes for a particular fluid to reach a particular concentration at a particular location in the biological specimen, such as the central region of the biological specimen. In some embodiments, the fluid comprises one or more fixatives (including any of those described herein). In other embodiments, the fluid comprises a dehydration reagent (e.g., gradient ethanol), a clearing agent (e.g., xylene), and paraffin used in embedding the biological specimen. Still other fluids are further described herein. While certain embodiments of the present disclosure may describe estimating the time for which a biological specimen can be optimally fixed when immersed in one or more fixatives, the present disclosure is applicable to predicting the optimal diffusion time for any fluid (e.g., ethanol, xylene, paraffin) through a biological specimen.

[0009] In some embodiments, systems and methods analyze components of the acquired TOF signal to determine whether the acquired TOF signal is deemed sufficiently accurate so that the time for optimal diffusion of fluid into the biological specimen can be predicted. In situations where the biological specimen is fixed with one or more fixatives, in some embodiments, systems and methods analyze different components of the acquired TOF signal to determine whether the acquired TOF signal is deemed sufficiently accurate to predict the time for optimal diffusion of the biological specimen by one or more fixatives, e.g., the time required to achieve a particular concentration or amount of one or more fixatives within or throughout the biological specimen, such as at the center of the biological specimen.

[0010] In view of the above, at least some of the embodiments of the present disclosure relate to systems and methods for acquiring TOF data through a biological specimen (e.g., such as that contained in a container such as a biopsy capsule or biopsy cassette) and analyzing the acquired TOF data, e.g., in real time. In some embodiments, the acquired TOF data is analyzed to determine whether the acquired TOF data at a particular time point is sufficiently accurate so that the TOF data at that particular time point can be used to calculate a time at which a fluid is optimally diffused into the biological specimen or a time at which the biological specimen is optimally fixed (e.g., with one or more of the fixatives described herein).

[0011] In view of the above, one aspect of the present disclosure is a method for estimating a time for a fluid to ideally diffuse into or throughout a biological specimen immersed in the fluid, the method including: acquiring acoustic data at one or more locations along the biological specimen immersed in the fluid; deriving time-of-flight (TOF) data from the acquired acoustic data, wherein the derived TOF data includes one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated decay constants; simultaneously calculating at least two reliability models based on the derived TOF data (e.g., the at least two reliability models include a past reliability model, a current reliability model, and / or a future reliability model); determining a time point at which the calculated at least two reliability models each independently satisfy a predetermined threshold criterion; and estimating the time for the fluid to ideally diffuse throughout the biological specimen based on the TOF data corresponding to the determined time point at which the calculated at least two reliability models each independently satisfy the predetermined threshold criterion.

[0012] In some embodiments, the fluid comprises one or more fixatives. In some embodiments, the one or more fixatives are aldehyde-based fixatives. In some embodiments, the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

[0013] In some embodiments, the at least two calculated confidence models include a historical confidence model and a current confidence model. In some embodiments, the at least two calculated confidence models include each of a historical confidence model, a current confidence model, and a future confidence model.

[0014] In some embodiments, the calculated historical reliability model satisfies the predetermined historical reliability model threshold criterion when a predetermined number of retrieved calculated candidate decay constants corresponding to a plurality of derived TOF data points are each determined to be within a predetermined threshold percentage value of the calculated average decay constant. In some embodiments, determining whether a predetermined number of retrieved calculated candidate decay constants are within a predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test. In some embodiments, performing the convergence test includes (i) calculating candidate decay constants for the candidate calculated TOF data points to provide a theoretical value, (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value, (iii) determining an actual percentage error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percentage error to a predetermined threshold percentage value. In some embodiments, the average decay constant is derived by: (i) retrieving the calculated decay constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the retrieved calculated decay constants. In some embodiments, the predetermined number of TOF data points preceding the candidate TOF data point is at least about 3. In some embodiments, the predetermined threshold percentage value is less than about 5%. In some embodiments, the predetermined threshold percentage value is less than about 2.5%.

[0015] In some embodiments, a calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold. In some embodiments, the current confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve, and (b) determining a confidence interval for the retrieved calculated decay constant based on the performed nonlinear regression fit.

[0016] In some embodiments, a calculated future confidence model satisfies a predetermined future confidence model threshold criterion if the calculated future confidence interval is below a predetermined future confidence model threshold. In some embodiments, the future confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using all calculated TOF data points of the calculated TOF curve, (b) determining a confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point, and (c) calculating a mean confidence interval of the TOF curve.

[0017] In some embodiments, the method further comprises staining the biological specimen for the presence of at least one biomarker, hi some embodiments, the at least one biomarker is a cancer biomarker.

[0018] In some embodiments, the method further comprises scoring the stained biological specimen for the presence of at least one biomarker.

[0019] In some embodiments, the method further comprises staining the biological specimen for the presence of at least two biomarkers, hi some embodiments, a single serial section from the biological specimen is stained for the presence of at least two biomarkers.

[0020] Another aspect of the present disclosure is a method for predicting a time to completion of fixation of a biological specimen immersed in one or more fixatives, the method comprising: acquiring acoustic data at one or more locations along the biological specimen immersed in the one or more fixatives; deriving time-of-flight (TOF) data from the acquired acoustic data, wherein the derived TOF data includes one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated decay constants; simultaneously calculating at least two reliability models based on the derived TOF data, wherein the at least two reliability models include a past reliability model, a current reliability model, and a future reliability model; and determining a time point at which the calculated at least two reliability models each independently satisfy a predetermined threshold criterion; and predicting the time to completion of fixation based on the TOF data corresponding to the determined time point at which the calculated at least two reliability models each independently satisfy the predetermined threshold criterion.

[0021] In some embodiments, the biological specimen is first immersed in one or more fixatives at a temperature below about 15°C, e.g., 14°C or less, 13°C or less, 12°C or less, 11°C or less, 10°C or less, 9°C or less, 8°C or less, 7°C or less, 6°C or less, 5°C or less, or 4°C or less. In some embodiments, acoustic data is acquired after the one or more fixatives are warmed (passively or actively) to a temperature above about 15°C, such as room temperature. In some embodiments, acoustic data is acquired after the one or more fixatives are warmed (passively or actively) to a temperature above about 25°C. In some embodiments, acoustic data is acquired after the one or more fixatives are warmed (passively or actively) to a temperature above about 35°C. In some embodiments, acoustic data is acquired after the one or more fixatives are warmed (passively or actively) to a temperature above about 45°C. In some embodiments, the acoustic data is acquired after warming (passively or actively) the one or more fixatives to a temperature of about 20° C. to about 50° C. In some embodiments, the acoustic data is acquired after warming (passively or actively) the one or more fixatives to a temperature of about 20° C. to about 45° C.

[0022] In some embodiments, the at least two calculated confidence models include a historical confidence model and a current confidence model. In some embodiments, the at least two calculated confidence models include each of a historical confidence model, a current confidence model, and a future confidence model.

[0023] In some embodiments, the calculated historical reliability model satisfies the predetermined historical reliability model threshold criterion when a predetermined number of retrieved calculated candidate decay constants corresponding to a plurality of derived TOF data points are each determined to be within a predetermined threshold percentage value of the calculated average decay constant. In some embodiments, determining whether a predetermined number of retrieved calculated candidate decay constants are within a predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test. In some embodiments, performing the convergence test includes (i) calculating candidate decay constants for the candidate calculated TOF data points to provide a theoretical value, (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value, (iii) determining an actual percentage error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percentage error to a predetermined threshold percentage value. In some embodiments, the average decay constant is derived by: (i) retrieving the calculated decay constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the retrieved calculated decay constants. In some embodiments, the predetermined number of TOF data points preceding the candidate TOF data point is at least 3. In some embodiments, the predetermined threshold percentage value is less than about 5%. In some embodiments, the predetermined threshold percentage value is less than about 2.5%.

[0024] In some embodiments, a calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold. In some embodiments, the current confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve, and (b) determining a confidence interval for the retrieved calculated decay constant based on the performed nonlinear regression fit.

[0025] In some embodiments, a calculated future confidence model satisfies a predetermined future confidence model threshold criterion if the calculated future confidence interval is below a predetermined future confidence model threshold. In some embodiments, the future confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using all calculated TOF data points of the calculated TOF curve, (b) determining a confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point, and (c) calculating a mean confidence interval of the TOF curve.

[0026] In some embodiments, the method further comprises staining the biological specimen for the presence of at least one biomarker, hi some embodiments, the at least one biomarker is a cancer biomarker.

[0027] In some embodiments, the method further comprises scoring the stained biological specimen for the presence of at least one biomarker, including any of the biomarkers listed herein.

[0028] In some embodiments, the method further comprises staining the biological specimen for the presence of at least two biomarkers, hi some embodiments, a single serial section from the biological specimen is stained for the presence of at least two biomarkers.

[0029] Another aspect of the present disclosure is a non-transitory computer-readable medium storing instructions for estimating a time for a fluid to ideally diffuse through a biological specimen immersed in the fluid, the non-transitory computer-readable medium including: deriving time-of-flight (TOF) data from acquired acoustic data; simultaneously calculating at least two reliability models based on the derived TOF data, where the at least two reliability models include a past reliability model, a current reliability model, and a future reliability model; determining a time point at which the calculated at least two reliability models each independently satisfy a predetermined threshold criterion; and estimating a time for the fluid to ideally diffuse through the biological specimen based on TOF data corresponding to the determined time point at which the calculated at least two reliability models each independently satisfy the predetermined threshold criterion.

[0030] In some embodiments, the method further includes instructions for calculating one or more decay constants. In some embodiments, the method further includes instructions for performing a convergence test. In some embodiments, the convergence test includes (i) calculating candidate decay constants for the candidate calculated TOF data points to provide a theoretical value, (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value, (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percent error to a predetermined threshold percentage value.

[0031] Another aspect of the present disclosure is a non-transitory computer-readable medium storing instructions for determining when at least two confidence models each independently satisfy a predetermined threshold criterion, the non-transitory computer-readable medium including: (a) deriving TOF data from acoustic data acquired from a biological specimen immersed in a fluid; (b) continuously calculating past confidence models, current confidence models, and future confidence models until each of the past, current, and future confidence models simultaneously and independently satisfy a predetermined threshold criterion; and (c) identifying a time point corresponding to the derived TOF data at which the past, current, and future confidence models simultaneously and independently satisfy the predetermined threshold criterion. In some embodiments, the models are continuously calculated as new data is received or acquired, which may occur every about 0.2 seconds to about 120 seconds as described herein.

[0032] In some embodiments, the calculated historical reliability model satisfies the predetermined historical reliability model threshold criterion when a predetermined number of retrieved calculated candidate decay constants corresponding to a plurality of derived TOF data points are each determined to be within a predetermined threshold percentage value of the calculated average decay constant. In some embodiments, determining whether a predetermined number of retrieved calculated candidate decay constants are within a predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test. In some embodiments, performing the convergence test includes (i) calculating candidate decay constants for the candidate calculated TOF data points to provide a theoretical value, (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value, (iii) determining an actual percentage error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percentage error to a predetermined threshold percentage value. In some embodiments, the average decay constant is derived by: (i) taking the calculated decay constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the taken calculated decay constants.

[0033] In some embodiments, a calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold. In some embodiments, the current confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve, and (b) determining a confidence interval for the retrieved calculated decay constant based on the performed nonlinear regression fit.

[0034] In some embodiments, a calculated future confidence model satisfies a predetermined future confidence model threshold criterion if the calculated future confidence interval is below a predetermined future confidence model threshold. In some embodiments, the future confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using all calculated TOF data points of the calculated TOF curve, (b) determining a confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point, and (c) calculating a mean confidence interval of the TOF curve.

[0035] In some embodiments, the non-transitory computer-readable medium further comprises instructions for estimating the time for a fluid to ideally diffuse into a biological specimen. In some embodiments, the fluid comprises one or more fixatives. In some embodiments, the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

[0036] Another aspect of the present disclosure is a system for estimating a time for a fluid to ideally diffuse through a biological specimen immersed in the fluid, the system comprising: (i) one or more processors; and (ii) one or more memories coupled to the one or more processors, the one or more memories storing computer-executable instructions that, when executed by the one or more processors, cause the system to perform operations including: deriving time-of-flight (TOF) data from acoustic data acquired at one or more positions along the biological specimen immersed in the fluid; simultaneously calculating at least two confidence models based on the derived TOF data, where the at least two confidence models include a past confidence model, a present confidence model, and a future confidence model; determining a time point at which the calculated at least two confidence models each independently satisfy a predetermined threshold criterion; and estimating a time for the fluid to ideally diffuse through the biological specimen based on TOF data corresponding to the determined time point at which the calculated at least two confidence models each independently satisfy the predetermined threshold criterion.

[0037] In some embodiments, the fluid comprises one or more fixatives, hi some embodiments, the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

[0038] In some embodiments, the calculated historical reliability model satisfies the predetermined historical reliability model threshold criterion when a predetermined number of retrieved calculated candidate decay constants corresponding to a plurality of derived TOF data points are each determined to be within a predetermined threshold percentage value of the calculated average decay constant. In some embodiments, determining whether a predetermined number of retrieved calculated candidate decay constants are within a predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test. In some embodiments, performing the convergence test includes (i) calculating candidate decay constants for the candidate calculated TOF data points to provide a theoretical value, (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value, (iii) determining an actual percentage error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percentage error to a predetermined threshold percentage value. In some embodiments, the average decay constant is derived by: (i) retrieving the calculated decay constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the retrieved calculated decay constants. In some embodiments, the predetermined number of TOF data points preceding the candidate TOF data point is at least 3. In some embodiments, the predetermined threshold percentage value is less than about 5%. In some embodiments, the predetermined threshold percentage value is less than about 2.5%.

[0039] In some embodiments, a calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold. In some embodiments, the current confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve, and (b) determining a confidence interval for the retrieved calculated decay constant based on the performed nonlinear regression fit.

[0040] In some embodiments, a calculated future confidence model satisfies a predetermined future confidence model threshold criterion if the calculated future confidence interval is below a predetermined future confidence model threshold. In some embodiments, the future confidence interval is calculated by (a) performing a nonlinear regression fit of the TOF curve using all calculated TOF data points of the calculated TOF curve, (b) determining a confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point, and (c) calculating a mean confidence interval of the TOF curve.

[0041] Applicants have surprisingly discovered that the systems and methods of the present disclosure provide accurate predictions of time to fixation of biological specimens. [Brief explanation of the drawings]

[0042] For a general understanding of the features of the present disclosure, reference is made to the drawings, wherein like reference numerals are used throughout to identify identical elements.

[0043] [Figure 1A] 1 provides a flowchart outlining the steps of a method of the present disclosure for predicting the diffusion time of a fluid (e.g., a fixative) into a biological specimen, according to one embodiment of the present disclosure. [Figure 1B] 1 provides a flowchart outlining the steps of a method of the present disclosure for predicting the diffusion time of a fluid (e.g., a fixative) into a biological specimen, according to one embodiment of the present disclosure. [Figure 2] 1 illustrates an exemplary digital pathology system including a computer system, according to some embodiments. [Figure 3] Various modules that may be utilized in a fluid diffusion and / or stationary prediction system are described, according to some embodiments. [Figure 4] 1 illustrates a method for calculating optimal fixation time for a biological specimen according to one embodiment of the present disclosure. [Figure 5A] A time course is provided showing how the baseline was calculated while scanning tonsil tissue for 0.4 hours. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 5B] A time course is provided showing how the baseline was calculated while scanning tonsil tissue for 0.8 hours. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 5C] A time course is provided showing how the baseline was calculated while scanning tonsil tissue for 1.2 hours. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 5D] A time course showing how the baseline was calculated while scanning tonsil tissue for 2.2 hours is provided. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data points acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 5E]A time course is provided showing how the baseline was calculated while scanning tonsil tissue for 2.83 hours. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 5F] A time course is provided showing how the baseline was calculated while scanning tonsil tissue for 4.84 hours. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed or dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 6A] Figure 5 shows an example of determining whether a TOF curve is valid for the same tonsil tissue specimen shown in Figure 5. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with a dashed / dotted line. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data point acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 6B] This figure shows an example of determining whether a TOF curve is valid for the same tonsil tissue sample shown in Figure 5. Graphs of each individual confidence model for all time points in the experiment where the criteria for high values were met and the criteria for low values were not met. In this particular example, all three criteria must be met simultaneously for the TOF signal to be considered representative of the actual diffusion profile of the tissue. (The top graph is the future confidence model, the middle graph is the past confidence model, and the bottom graph is the current confidence model.) [Figure 7A]Figure 1 shows an example of determining whether a TOF curve is valid in colon tissue. Top plot: TOF curve with black circles representing single TOF data points, best fit curve is depicted with a solid black line. 95% confidence intervals are shown with dashed / dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constants calculated using the most recent data points acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 7B] An example of determining whether a TOF curve is valid for colon tissue is shown. Graphs of each individual confidence model for all time points in an experiment where the criteria for high values are met and the criteria for low values are not met. All three criteria must be met simultaneously for the TOF signal to be considered representative of the tissue's actual diffusion profile. (The top graph is the future confidence model, the middle graph is the past confidence model, and the bottom graph is the current confidence model.) [Figure 8A] Provides an example of determining whether a TOF curve is valid in kidney tissue. Top plot: TOF curve with black circles representing single TOF data points; the best fit curve is depicted with a solid black line. The 95% confidence interval is shown with dashed / dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data points acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 8B] An example is provided for determining whether a TOF curve is valid for kidney tissue. Graphs of each individual confidence model for all time points in an experiment where the criteria for high values are met and the criteria for low values are not met. In this particular example, all three criteria must be met simultaneously for the TOF signal to be considered representative of the tissue's actual diffusion profile. (The top graph is the future confidence model, the middle graph is the past confidence model, and the bottom graph is the current confidence model.) [Figure 9A]Figure 1 shows an example of determining whether a TOF curve is valid in breast tissue. Top plot: TOF curve with black circles representing single TOF data points, best fit curve is depicted with a solid black line. 95% confidence interval is shown with dashed / dotted lines. Top-center plot: Width of the 95% confidence interval for the TOF curve. Bottom-center plot: Candidate decay constant calculated using the most recent data points acquired. Bottom plot: 95% confidence interval for the decay constant. [Figure 9B] Shows an example of determining whether a TOF curve is valid for breast tissue. Graphs of each individual confidence model for all time points in an experiment where the criteria for high values are met and the criteria for low values are not met. All three criteria must be met simultaneously for the TOF signal to be considered representative of the tissue's actual diffusion profile. (The top graph is the future confidence model, the middle graph is the past confidence model, and the bottom graph is the current confidence model.) [Figure 10A] We present the results of the TOF analysis. Distribution of TOF diffusion data for multiple tissues, demonstrating that the acoustic technique works across multiple tissue types. [Figure 10B] The results of a TOF analysis are presented. Cumulative results for criteria development were obtained by analyzing 105 tissues across multiple tissue types using the presented method for determining when a TOF curve is valid. The time to a valid fit is plotted on the horizontal axis against the calculated optimal fixation time on the vertical axis. [Figure 11A] An analysis of the accuracy of the disclosed reliability model is provided. The distribution of the model's prediction error, calculated as the difference between the optimal diffusion time calculated at the time the signal was determined to be valid and the end of the experiment. [Figure 11B] An analysis of the accuracy of the disclosed confidence model is provided. The average 95% confidence interval for all 105 tissues in the study for the entire TOF signal, as well as the decay constant at which the signal was considered valid by the model. [Figure 12A]Figure 1 shows the correlation between NBF diffusion time and staining quality. H&E images acquired at different cold soak times in NBF. Based on H&E-based morphology, cold soak times of 1.5, 3, and 5 hours produced heavily structured, borderline, and exemplary staining, respectively. [Figure 12B] Figure 1 shows the correlation between NBF diffusion time and staining quality. An example depiction of a TOF diffusion curve from active NBF diffusion immediately after immersion of tissue in NBF, showing a rapidly changing TOF signal. Conversely, after several hours, the diffusion rate in the tissue slowed significantly as the tissue and NBF approached osmotic equilibrium. [Figure 13A] Figure 1 shows the adjusted rate of diffusion metrics for predicting staining quality. Normalized gradients of tonsillar tissue for 3 and 5 hours in cold NBF, when the tissue is expected to have borderline and ideal staining, respectively. Different threshold diffusivities of -7.4% / hr, -8.0% / hr, and -10.4% / hr are indicated by 3, 2, and 1, respectively. [Figure 13B] Figure 1 shows the rate of adjustment of diffusion metrics to predict staining quality. Average TOF signal from a 6 mm tonsil with the approximate location of threshold diffusivity indicated. [Figure 13C] Figure 1 shows the rate of adjustment of diffusion metrics to predict staining quality. Plot of predicted completion time for each of the three evaluated threshold diffusion rates. [Figure 13D] Figure 1 shows the rate of adjustment of diffusion metrics to predict staining quality. Plot of predicted completion time for each of the three evaluated threshold diffusion rates. [Figure 13E] Figure 1 shows the rate of adjustment of diffusion metrics to predict staining quality. Plot of predicted completion time for each of the three evaluated threshold diffusion rates. [Figure 14A]An example statistical model for validating TOF diffusion signals is described. TOF diffusion curves show how the validation algorithm works during real-time data acquisition. As data is collected, three statistical algorithm "confidence models" continuously monitor signal fidelity by focusing on past, present, and future aspects of the data (past confidence model, current confidence model, future confidence model). The time (confidence model) that each algorithm satisfies is labeled with a solid horizontal line on the plot in order of convergence time. When all three conditions for data fidelity are met, the data represent the actual diffusion rate of the tissue, and the required diffusion time of the tissue is calculated. [Figure 14B] An example of a statistical model for validating TOF diffusion signals is described. A detailed diagram of the current algorithm is presented. [Figure 14C] An example of a statistical model for validating TOF diffusion signals is described. A detailed diagram of the future algorithm is presented. [Figure 14D] An example of a statistical model for validating TOF diffusion signals is described. A detailed diagram of the previous algorithm is shown. [Figure 14E] 14E , an example of a statistical model for validating TOF diffusion signals is provided. Plot of time versus predicted fixation time for validating diffusion signals for 105 tissues. On average (asterisks in FIG. 14E ), it took 2.04 hours for the detected diffusion profile to converge to the actual diffusion rate of the tissue, and the tissue was predicted to require 2.96 hours of diffusion in cold formalin. [Figure 15] A schematic diagram of the radial image analysis workflow used to analyze the effect of formalin diffusion on functional IHC staining for FOXP3 is provided. [Figure 16] Comparative examples of staining results for different fixation protocols are provided. Left column) Brightfield whole slide scans of tissues. Center left column) Heat maps of FOXP3 positivity for each tissue. Center right column) Graphical depiction of radial zones within each tissue showing areas of poor and adequate staining, defined by DAB positivity within 50% of the edge value. Right column) Histograms of FOXP3 staining versus distance to the edge of the tissue. [Figure 17A] Figure 1 shows a quantitative analysis of FOXP3 staining for different fixation protocols. Depth of penetration of adequate FOXP3 staining, defined as the distance into the tissue at which the staining drops to half of its edge value. [Figure 17B] Quantitative analysis of FOXP3 staining for different fixation protocols. Percentage of tissue showing adequate FOXP3 staining. [Figure 17C] Figure 1 shows a quantitative analysis of FOXP3 staining for different fixation protocols. Normalized FOXP3 expression plotted against distance into the tissue. Solid line indicates p<0.002 by Welch's two-tailed t-test. [Figure 18A] Quantitative bcl-2 expression following different fixation protocols. Depth of penetration of appropriate bcl-2 staining. [Figure 18B] Quantitative bcl-2 expression for different fixation protocols is shown. Area of adequate bcl-2 staining. [Figure 18C] Quantitative bcl-2 expression for different fixation protocols is shown. Normalized bcl-2 expression plotted against distance into the tissue. [Figure 19A] Quantitative analysis of staining intensity for different fixation protocols is provided. Normalized intensity of FOXP3 staining plotted against distance into the tissue. [Figure 19B] Quantitative analysis of staining intensity for different fixation protocols is provided. Normalized intensity of bcl-2 staining plotted against distance into the tissue. [Figure 20] 1 shows the predicted optimal fixation times for multiple tissue types based on the developed prediction algorithm. [Figure 21] In some aspects of the present disclosure, a flowchart illustrating the steps performed by the historical confidence module is provided. [Figure 22] In some aspects of the present disclosure, a flowchart illustrating the steps performed by the current confidence module is provided. [Figure 23]In some aspects of the present disclosure, a flowchart illustrating the steps performed by the future confidence module is provided. DETAILED DESCRIPTION OF THE INVENTION

[0044] It is also to be understood that, unless expressly stated to the contrary, in any method claimed herein that includes more than one step or act, the order of the method steps or acts is not necessarily limited to the order in which the method steps or acts are described.

[0045] References herein to "one embodiment," "embodiment," "exemplary embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but all embodiments may or may not include that particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed to be within the knowledge of one of ordinary skill in the art to affect such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly stated.

[0046] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly dictates otherwise. The term "comprising" is defined inclusively, such that "including A or B" means including A, B, or A and B.

[0047] As used in this specification and the claims, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted as inclusive, e.g., including at least one element or list of elements, but also including more than one element or list of elements, and optionally, including additional items not listed. Only terms clearly indicated to the contrary, such as "only one of" or "exactly one of," or, when used in the claims, "consisting of," shall refer to the inclusion of exactly one element of an element or list of elements. In general, the term "or" as used herein shall only be interpreted as indicating exclusive alternatives (e.g., "one or the other, but not both") when preceded by terms of exclusivity, such as "either," "one of," "only one of," or "exactly one of." "Consisting essentially of," when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0048] Terms such as "comprising," "including," and "having" are used interchangeably and have the same meaning. Similarly, "comprises," "includes," "has," and the like are used interchangeably and have the same meaning. Specifically, each term is defined consistent with the general U.S. patent law definition of "comprising," and therefore is to be interpreted as open term meaning "at least the following" and not excluding additional features, limitations, aspects, etc. Thus, for example, "an apparatus having components a, b, and c" means that the apparatus includes at least components a, b, and c. Similarly, the phrase: "a method including steps a, b, and c" means that the method includes at least steps a, b, and c. Additionally, although steps and processes may be outlined in a particular order herein, those skilled in the art will recognize that the ordering of steps and processes may vary.

[0049] As used herein in the specification and claims, the phrase "at least one" in connection with a list of one or more elements should be understood to mean at least one element selected from any one or more elements of the list of elements, but not necessarily including at least one of each and every element specifically listed in the list of elements, and not excluding any combination of elements in the list of elements. This definition also allows for elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether related to those elements specifically identified or not, may optionally be present. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B," or, equivalently, "at least one of A and / or B") can refer in one embodiment to at least one A, optionally including more than one, with no B (and optionally including elements other than B); in another embodiment to at least one B, optionally including more than one, with no A (and optionally including elements other than A); in yet another embodiment to at least one A, optionally including more than one, and at least one B, optionally including more than one (and optionally including other elements); and so forth.

[0050] As used herein, the terms "biological specimen," "tissue specimen," and the like refer to any sample containing biomolecules (such as proteins, peptides, nucleic acids, lipids, carbohydrates, or combinations thereof) obtained from any organism, including viruses. Other examples of organisms include mammals (such as humans, veterinary animals such as cats, dogs, horses, cows, and pigs, and laboratory animals such as mice, rats, and primates), insects, annelids, arachnids, marsupials, reptiles, amphibians, bacteria, and fungi. Biological specimens include tissue samples (such as tissue sections or needle biopsies of tissue), cell samples (such as cytological smears, such as Pap smears or blood smears, or samples of cells obtained by microdissection), or cell fractions, fragments, or organelles (obtained by lysing cells and separating their components, such as by centrifugation). Other examples of biological specimens include blood, serum, urine, semen, feces, cerebrospinal fluid, interstitial fluid, mucous membranes, tears, sweat, pus, biopsy tissue (e.g., obtained by surgical or needle biopsy), nipple aspirate, earwax, milk, vaginal fluid, saliva, swabs (such as cheek swabs), or any material containing biomolecules derived from an initial biological specimen. In certain embodiments, the term "biological specimen," as used herein, refers to a sample prepared from a tumor or portion thereof obtained from a subject (such as a homogenized or liquefied sample).

[0051] As used herein, the term "biomarker" or "marker" refers to a measurable indicator of some biological state or condition. In particular, a biomarker may be a protein or peptide, such as a surface protein, that can be specifically stained and indicates a biological characteristic of a cell, such as the cell type or physiological state of the cell. Immune cell markers are biomarkers that selectively indicate characteristics related to a mammalian immune response. Biomarkers can be used to determine how well the body responds to treatment of a disease or condition, or whether a subject is susceptible to a disease or condition. In the context of cancer, a biomarker refers to a biological substance that indicates the presence of cancer in the body. A biomarker can be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Genetic biomarkers, epigenetic biomarkers, proteomic biomarkers, glycomic biomarkers, and imaging biomarkers can be used for cancer diagnosis, prognosis, and epidemiology. Such biomarkers can be assayed in non-invasively collected biological fluids such as blood or serum. Several gene- and protein-based biomarkers are already used in patient care, including, but not limited to, AFP (liver cancer), BCR-ABL (chronic myeloid leukemia), BRCA1 / BRCA2 (breast cancer / ovarian cancer), BRAF V600E (melanoma / colorectal cancer), CA-125 (ovarian cancer), CA19.9 (pancreatic cancer), CEA (colorectal cancer), EGFR (non-small cell lung cancer), HER-2 (breast cancer), KIT (gastrointestinal stromal tumor), PSA (prostate-specific antigen), S100 (melanoma), etc. Biomarkers can be useful as diagnostics (to identify early cancer) and / or prognostic agents (to predict how aggressive a cancer will be and / or how a subject will respond to a particular treatment and / or how likely the cancer is to recur).

[0052] As used herein, the term "cell" refers to a prokaryotic or eukaryotic cell. The cell can be an adherent or non-adherent cell, e.g., an adherent prokaryotic cell, an adherent eukaryotic cell, a non-adherent prokaryotic cell, or a non-adherent eukaryotic cell. The cell can be a yeast cell, a bacterial cell, an algal cell, a fungal cell, or any combination thereof. The cell can be a mammalian cell. The cell can be a primary cell obtained from a subject. The cell can be a cell line or an immortalized cell. The cell can be obtained from a mammal, such as a human or a rodent. The cell can be a cancer or tumor cell. The cell can be an epithelial cell. The cell can be a red blood cell or a white blood cell. The cell can be an immune cell, such as a T cell, a B cell, a natural killer (NK) cell, a macrophage, or a dendritic cell. The cell can be a neuron, a glial cell, an astrocyte, a neuron-supporting cell, a Schwann cell, or the like. The cell can be an endothelial cell. The cell can be a fibroblast or a keratinocyte. The cell can be a pericyte, hepatocyte, stem cell, progenitor cell, etc. The cell can be a circulating cancer or tumor cell or a metastatic cell. The cell can be a marker-specific cell, such as a CD8+ T cell or a CD4+ T cell. The cell can be a neuron. The neuron can be a central neuron, a peripheral neuron, a sensory neuron, an interneuron, an intraneuron, a motor neuron, a multipolar neuron, a bipolar neuron, or a pseudounipolar neuron. The cell can be a neuron-supporting cell, such as a Schwann cell. The cell can be one of the cells of the blood-brain barrier system. The cell can be a cell line, such as a neuronal cell line. The cell can be a primary cell, such as a cell obtained from the brain of a subject. The cell can be a population of cells that can be isolated from a subject, such as a tissue biopsy, a cytology specimen, a blood sample, a fine needle aspirate (FNA) sample, or any combination thereof. The cells may be obtained from bodily fluids such as urine, milk, sweat, lymph, blood, sputum, amniotic fluid, aqueous humor, vitreous humor, bile, cerebrospinal fluid, chyle, chyme, exudate, endolymph, perilymph, gastric acid, mucus, pericardial fluid, ascites, pleural fluid, pus, mucus, saliva, sebum, serous fluid, phlegm, tears, vomit, or other bodily fluids. The cells may include cancerous cells, non-cancerous cells, tumor cells, non-tumor cells, healthy cells, or any combination thereof.

[0053] As used herein, the term "diffusion coefficient" or "diffusion constant" refers to the proportionality constant between the molar flux due to molecular diffusion and the concentration gradient (or driving force) of the substance through which diffusion is observed. Diffusivity is encountered, for example, in Fick's law and many other equations in physical chemistry. The higher the diffusivity (of one substance relative to another), the faster they diffuse into each other. Typically, the diffusion constant of a compound is about 10,000 times greater in air than in water. For example, the diffusion constant of carbon dioxide in air is 16 mm2 / s, and the diffusion constant of carbon dioxide in water is 0.0016 mm2 / s.

[0054] As used herein, the term "fluid" refers to one or more liquids or liquid compositions, including reagents, solvents, solutions (e.g., polar solvents, non-polar solvents), mixtures, and the like. Fluids may be aqueous or non-aqueous. Non-limiting examples of fluids include solvents and / or solutions for deparaffinizing paraffin-embedded biological specimens or hydrocarbons (e.g., alkanes, isoalkanes, and aromatic compounds, such as xylene). Yet another example of a fluid includes solvents (and mixtures thereof) used to dehydrate or rehydrate biological specimens. In some embodiments, the fluid comprises one or more glycol ethers, such as one or more propylene-based glycol ethers (e.g., propylene glycol ether, di(propylene glycol) ether, and tri(propylene glycol) ether), ethylene-based glycol ethers (e.g., ethylene glycol ether, di(ethylene glycol) ether, and tri(ethylene glycol) ether), and functional analogs thereof. Yet other solutions are described in U.S. Patent Application Publication No. 2016 / 0282374, the disclosure of which is incorporated herein by reference in its entirety.

[0055] As used herein, the term "fixation" refers to a process in which the molecular and / or morphological details of a cell sample are preserved. Generally, there are three types of fixation processes: (1) heat fixation, (2) perfusion, and (3) immersion. In heat fixation, the sample is exposed to a heat source for a sufficient time to heat-kill and adhere the sample to a slide. Perfusion involves using the vascular system to distribute a chemical fixative throughout an organ or organism. Immersion involves immersing the sample in a volume of chemical fixative and allowing the fixative to diffuse throughout the sample. Chemical fixation involves the diffusion or perfusion of chemicals throughout the cell sample, where the fixative reagent induces a reaction that preserves the structure (both chemical and structural) as closely as possible to that of the live cell sample.

[0056] Fixatives (or "fixative solutions") can be categorized into two broad classes based on their mode of operation: cross-linking fixatives and non-cross-linking fixatives. Cross-linking fixatives, typically aldehydes, form covalent chemical bonds between endogenous biomolecules, such as proteins and nucleic acids, present in a tissue sample. In some embodiments, the fixative is an aldehyde-based cross-linking fixative, such as glutaraldehyde and / or formalin-based solutions. Examples of aldehydes frequently used for immersion fixation include formaldehyde (standard working concentrations of about 5 to about 10% formalin for most tissues, although concentrations as high as about 20% formalin have been used for certain tissues), glyoxal (standard working concentration 17 to 86 mM), and glutaraldehyde (standard working concentration 200 mM).

[0057] In some embodiments, aldehydes are often used in combination with one another. A standard aldehyde combination includes 10% formalin + 1% (w / v) glutaraldehyde. Atypical aldehydes, including fumaraldehyde, 12.5% hydroxyadipaldehyde (pH 7.5), 10% crotonaldehyde (pH 7.4), 5% pyruvic aldehyde (pH 5.5), 10% acetaldehyde (pH 7.5), 10% acrolein (pH 7.6), and 5% methacrolein (pH 7.6), have been used for certain specialized fixative applications. Other specific examples of aldehyde-based fixative solutions used in immunohistochemistry are listed in Table 1: [Table 1] TIFF0007720412000002.tif61164

[0058] Formaldehyde is the most commonly used crosslinking fixative in histology. Formaldehyde can be used at various concentrations for fixation, but it is primarily used as 10% neutral buffered formalin (NBF), which is approximately 3.7% formaldehyde in aqueous phosphate-buffered saline. Paraformaldehyde is a polymerized form of formaldehyde that depolymerizes upon heating to provide formalin. Glutaraldehyde acts similarly to formaldehyde but is a larger molecule with a slower rate of diffusion across membranes. Glutaraldehyde fixation provides a more tightly or tightly bound fixation product, causes rapid and irreversible changes, fixes quickly and well at 4°C, and provides good overall cytoplasmic and nuclear detail, but is not ideal for immunohistochemical staining. Some fixation protocols use a combination of formaldehyde and glutaraldehyde. Glyoxal and acrolein are less commonly used aldehydes. Denaturing fixatives (typically alcohol or acetone) work by displacing water in cell samples, destabilizing hydrophobic and hydrogen bonds within proteins, causing otherwise water-soluble proteins to become water-insoluble and precipitate, a process that is largely irreversible.

[0059] As used herein, the term "formalin-fixed paraffin-embedded (FFPE) tissue section" refers to a section of tissue, e.g., biopsy material, obtained from a subject, fixed in formaldehyde (e.g., 3%-5% formaldehyde in phosphate-buffered saline) or Bouin's solution, embedded in wax, cut into thin sections, and then mounted on a flat surface, e.g., a microscope slide.

[0060] As used herein, the term "immunohistochemistry" refers to a method for determining the presence or distribution of an antigen in a sample by detecting the interaction of the antigen with a specific binding agent, such as an antibody. The sample is contacted with an antibody under conditions that allow antibody-antigen binding. Antibody-antigen binding can be detected by a detectable label conjugated to the antibody (direct detection) or by a detectable label conjugated to a secondary antibody that specifically binds to the primary antibody (indirect detection). In some instances, indirect detection can involve a third or more antibodies, which serve to further enhance the detectability of the antigen. Examples of detectable labels include enzymes, fluorophores, and haptens, which, in the case of enzymes, can be used with chromogenic or fluorogenic substrates.

[0061] As used herein, the term "slide" refers to any substrate of any suitable dimensions (e.g., a substrate made entirely or partially of glass, quartz, plastic, silicon, etc.) on which a biological specimen is placed for analysis, more specifically, a "microscope slide" such as a standard 3" x 1" microscope slide or a standard 75mm x 25mm microscope slide. Examples of biological specimens that may be placed on a slide include, but are not limited to, cytological smears, thin tissue sections (such as from a biopsy), and arrays of biological specimens, e.g., tissue arrays, cell arrays, DNA arrays, RNA arrays, protein arrays, or any combination thereof. Thus, in one embodiment, tissue sections, DNA samples, RNA samples, and / or proteins are placed at specific locations on the slide. In some embodiments, the term slide may refer to SELDI and MALDI chips, as well as silicon wafers.

[0062] As used herein, the term "specific binding entity" refers to a member of a specific binding pair. A specific binding pair is a pair of molecules that are characterized by binding to each other to the substantial exclusion of binding to other molecules (e.g., a specific binding pair has a binding constant at least 10 times higher than the binding constant of either of the two members of the binding pair to other molecules in a biological sample). 3 M -1 Large, 10 4 M -1 Big or 10 5 M -1 (They can have large binding constants.) Examples of specific binding moieties include specific binding proteins (e.g., antibodies, lectins, avidins such as streptavidin, and protein A). Specific binding moieties can also include molecules (or portions thereof) that are specifically bound by such specific binding proteins. Specific binding entities include the primary antibodies or nucleic acid probes described above.

[0063] As used herein, the terms "staining," "staining," and the like generally refer to any treatment of a biological specimen to detect and / or differentiate the presence, location, and / or amount (e.g., concentration) of a specific molecule (e.g., lipid, protein, or nucleic acid) or a specific structure (e.g., normal or malignant cells, cytosol, nucleus, Golgi apparatus, or cytoskeleton) in the biological specimen. For example, staining can provide contrast between a specific molecule or specific cellular structure and the surrounding area of the biological specimen, and the intensity of the staining can provide a measure of the amount of a specific molecule in the specimen. Staining can be used to aid in the observation of molecules, cellular structures, and organisms using not only brightfield microscopes but also other observation tools such as phase-contrast microscopes, electron microscopes, and fluorescence microscopes. Some staining performed by the system can be used to visualize cell contours. Other staining performed by the system can depend on the specific cellular component (e.g., molecule or structure) being stained, with no or relatively little staining of other cellular components. Examples of types of staining methods performed by the system include, but are not limited to, histochemical methods, immunohistochemical methods, and other methods based on reactions between molecules (including non-covalent interactions), such as hybridization reactions between nucleic acid molecules. Staining methods include, but are not limited to, primary staining methods (e.g., H&E staining, Pap staining, etc.), enzyme-linked immunohistochemical methods, and in situ RNA and DNA hybridization methods, such as fluorescent in situ hybridization (FISH).

[0064] As used herein, the term "substantially" refers to the qualitative condition of exhibiting a total or nearly total extent or degree of a characteristic or property of interest. In some embodiments, "substantially" means within about 20%. In some embodiments, "substantially" means within about 15%. In some embodiments, "substantially" means within about 10%. In some embodiments, "substantially" means within about 5%. In some embodiments, "substantially" means within about 2.5%. In some embodiments, "substantially" means within about 2%. In some embodiments, "substantially" means within about 1.5%. In some embodiments, "substantially" means within about 1%.

[0065] As used herein, the term "target" refers to any molecule for which the presence, location, and / or concentration is or can be determined. Examples of target molecules include proteins, epitopes, nucleic acid sequences, and haptens, such as haptens covalently bound to proteins. Target molecules are typically detected using one or more conjugates of a specific binding molecule and a detectable label.

[0066] As used herein, the term "time of flight" ("TOF") refers to the time it takes an object, particle, or wave (e.g., acoustic wave, electromagnetic wave, etc.) to travel a distance through a medium. TOF may be measured empirically, for example, by determining the phase difference between an acoustic signal emitted by a transmitter ("transmitted signal") and an acoustic signal received by a receiver ("received signal"). TOF information may then be used to learn about the properties (such as the composition of the material) of a material (e.g., a biological specimen) placed in the medium. For example, TOF may be used to determine the diffusion of a fixative (e.g., formalin) into a biological specimen (e.g., a tissue sample). Formalin diffuses into tissue sections and crosslinks proteins and nucleic acids, thereby halting metabolism, preserving biomolecules, and preparing the tissue for paraffin wax infiltration. In some embodiments, an algorithm may be utilized to determine how much diffusion should be allowed in the fixative to ensure optimal fixation and therefore proper and ideal staining.

[0067] overview

[0068] The present disclosure provides systems and methods that facilitate prediction of the estimated time for a fluid to optimally diffuse through a biological specimen, e.g., the estimated time it takes for the fluid to reach a specific concentration at a specific location in a tissue sample, e.g., the central region of the biological specimen. In some embodiments, the fluid includes one or more fixatives (including any of those described herein). In other embodiments, the fluid includes a dehydration reagent (e.g., gradient ethanol), a clearing agent (e.g., xylene), and paraffin used to embed the biological specimen.

[0069] In some embodiments, the fluid comprises 10% NBF. In some embodiments, the fluid comprises about 70% ethanol. In some embodiments, the fluid comprises about 80% ethanol. In some embodiments, the fluid comprises about 90% ethanol. In some embodiments, the fluid comprises about 100% ethanol. In some embodiments, the fluid comprises xylene. In some embodiments, the fluid comprises paraffin.

[0070] The degree of fluid diffusion into a biological specimen may affect downstream processing and analytical methods. For example, in the context of biological specimen fixation quality, current clinical practice dictates controlling the fixation duration to achieve a compromise between preserving tissue morphology and loss of antigenicity. Indeed, too short or too long a fixation duration can adversely affect downstream sample processing. Therefore, there remains a need for accurate prediction of the time at which a biological specimen is optimally fixed before downstream processing, e.g., before contacting the biological specimen with one or more specific binding entities. Applicant surprisingly discovered that the disclosed systems and methods provide accurate prediction of the time at which a fluid (e.g., one or more fixatives) is optimally diffused into a biological specimen, e.g., a tissue specimen or a cytological specimen.

[0071] In view of the above, and in the context of fixation of biological specimens, the present disclosure provides systems and methods that facilitate prediction of the estimated time for which a biological specimen, such as a tissue sample derived from a human subject, will be optimally fixed (also referred to herein as "time to fixation" or "time to complete fixation"). In some embodiments, the prediction of the time for which the biological specimen will be optimally fixed is based on TOF data acquired at specific time points during the fixation process (e.g., a single-temperature fixation process or a two-temperature fixation process) that is deemed sufficiently accurate (i.e., the TOF data at the specific time point meets predetermined criteria indicating it is effective and suitable for downstream processing) so that the time required to achieve a particular concentration or amount of fixative within the biological specimen can be predicted. Determining the "time to complete fixation" (e.g., using the prediction module 204) and determining what TOF data is "deemed sufficiently accurate" for making the prediction (e.g., using the signal modeling module 203) are further described herein.

[0072] 1A and 1B, at least some embodiments of the present disclosure relate to systems and methods for (i) acquiring TOF data through a biological specimen, e.g., at multiple locations across the biological specimen (step 101), and (ii) analyzing the acquired TOF data, e.g., in real time, while the biological specimen is immersed in a fluid, e.g., one or more fixatives. In some embodiments, the fluid includes one or more fixatives, and the biological specimen is immersed in the one or more fixatives while acoustic data is collected, e.g., continuously (e.g., every about 0.2 seconds to about 120 seconds). In some embodiments, the acquired TOF data is analyzed in real time to determine whether the acquired TOF data at a particular time point is deemed sufficiently accurate (step 102), so that the TOF data at that particular time point can be used to estimate a time for optimal diffusion of the fluid into the biological specimen, e.g., if the fluid includes one or more fixatives to estimate a time for optimal fixation of the biological specimen (step 105).

[0073] In some embodiments, determining whether TOF data acquired at a particular time point is deemed sufficiently accurate includes calculating at least two different reliability models, e.g., past and current reliability models, so that the TOF data at that particular time point can be used to estimate the time at which the fluid optimally diffuses into the biological specimen or the time at which the biological specimen is optimally immobilized. In some embodiments, three different reliability models are calculated (see steps 103A, 103B, and 103C). In some embodiments, the at least two reliability models are recalculated each time new TOF data is collected. If at least two reliability models are satisfied (step 104) (e.g., simultaneously by determining whether the received data independently meets predetermined threshold criteria for each model), the signal is deemed accurate, and the time at which the fluid optimally diffuses into the biological specimen or the time at which the biological specimen is optimally immobilized can be estimated (step 105).

[0074] In some embodiments, once at least two confidence models are satisfied, the systems and methods described herein facilitate determining the moment when the TOF signal from the biological specimen truly represents the actual diffusion rate (present and future) and predict the time when the fluid will optimally diffuse into the biological specimen or when the biological specimen will be properly fixed. In some embodiments, if the time when the TOF data is deemed sufficiently accurate is in the future compared to the time when the TOF data is deemed sufficiently accurate, the biological specimen is left in the fluid, such as one or more fixative solutions. In other embodiments, if the time when the TOF data is deemed sufficiently accurate is in the past compared to the time when the TOF data is deemed sufficiently accurate, the biological specimen is removed from the fluid, such as one or more fixative solutions. In some embodiments, the accuracy of the prediction can be determined by comparing the model's ground truth determination to an experimentally validated ground truth, i.e., how much the fit changes after the model predicts it to be stable.

[0075] 2 and 3 , system 200 includes a signal acquisition module 201 including one or more transmitters and / or one or more receivers (described further herein). In some embodiments, signal acquisition module 201 is communicatively coupled to computer 100. Computer system 100 may include digital electronic circuitry, firmware, hardware, one or more memories 205, computer storage media (e.g., storage module 240), a computer program or set of instructions (e.g., a program stored in a memory or storage medium), one or more processors 206 (including a programmed processor), and any other hardware, software, firmware modules, or combinations thereof (as described further herein), such as a desktop computer, laptop computer, tablet, etc. In some embodiments, signal acquisition module 201 may be coupled to computer 100 locally or via network 120.

[0076] In some embodiments, system 200 described herein may be communicatively coupled to additional components, such as a server, database, microscope, imaging device, scanner, other imaging system, automated slide preparation device, etc. These additional components are described herein. For example, system 200 may be coupled to an automated slide preparation device such that an optimally fixed biological specimen (determined according to the methods of the present disclosure) may be stained, such as immunoenzymatically, for the presence of one or more biomarkers (non-limiting examples of suitable biomarkers are described herein).

[0077] As another example, system 200 may further include an imaging device (e.g., to acquire images of a biological specimen immunoenzymatically stained for the presence of one or more biomarkers after the biological specimen has been optimally fixed), and images captured from the imaging device may be stored in binary format for further processing and / or analysis, such as locally or on a server. In some embodiments, the imaging device (or other image source, including pre-scanned images stored in memory) may include, but is not limited to, one or more image capture devices. Image capture devices include, but are not limited to, cameras (e.g., analog cameras, digital cameras, etc.), optical systems (e.g., one or more lenses, a sensor focus lens group, a microscope objective, etc.), imaging sensors (e.g., charge-coupled devices (CCDs), complementary metal-oxide semiconductor (CMOS) image sensors, etc.), photographic film, etc. In digital embodiments, the image capture device may include multiple lenses that cooperate to demonstrate on-the-fly focusing. The image sensor, e.g., a CCD sensor, may capture digital images of the specimen. In some embodiments, the imaging device is a bright-field imaging system, a multispectral imaging (MSI) system, or a fluorescence microscopy system. In some embodiments, the image data may be generated by an image scanning system, such as a VENTANA DP200 scanner by VENTANA MEDICAL SYSTEMS, Inc. (Tucson, Arizona), or other suitable imaging device. Additional imaging devices and systems are further described herein. Those skilled in the art will understand that digital color images acquired by imaging devices are traditionally composed of primary color pixels. Each colored pixel may be coded with three digital components, each containing the same number of bits, and each component generally corresponds to a primary color—red, green, or blue—also referred to by the term "RGB" components.

[0078] 3 provides an overview of the system 200 of the present disclosure and the various modules utilized within the system 200. In some embodiments, the system 200 employs a computing device or computer-implemented method having one or more processors 206 and one or more memories 205 that store non-transitory computer-readable instructions for execution by the one or more processors to cause the one or more processors to perform certain instructions described herein. As described above, the system 200 of the present disclosure can be utilized to predict the fixation time of a biological specimen, i.e., the time at which the biological specimen is optimally fixed. Similarly, the system 200 of the present disclosure can be utilized to predict the time at which a fluid is optimally diffused into a biological specimen, such as a tissue sample.

[0079] 3 and 4 , in some embodiments, system 200 includes a signal acquisition module 201 adapted to acquire an acoustic data set (step 401). In some embodiments, system 200 of the present disclosure further includes a signal processing module 202 that receives acoustic data from signal acquisition module 201 (or from memory 205 or storage subsystem 240 in communication therewith) and processes the received acoustic data (e.g., signal processing module 202 may process the acoustic data set to generate TOF data including one or more TOF data points, one or more TOF curves, one or more decay constants, etc.) (step 402). In some embodiments, the TOF data (e.g., output of signal processing module 202 in the form of TOF data and / or TOF curves) is stored in memory 205 or storage subsystem 240 so that it can be retrieved as input by either signal modeling module 203 or prediction module 204.

[0080] In some embodiments, system 200 further includes a signal modeling module 203 for evaluating whether the acquired TOF data (which in some embodiments includes one or more TOF curves) accurately reflects the actual diffusion rate of a fluid (e.g., one or more fixatives) through the biological specimen. In some embodiments, signal modeling module 203 receives TOF data points (e.g., a predetermined number of TOF data points), one or more derived TOF curves, and / or one or more calculated decay constants from signal processing module 202 (or memory 205 or storage subsystem 240 in communication therewith) and calculates at least two different confidence models (step 403).

[0081] In some embodiments, the signal modeling module 203 calculates at least two different reliability models, or calculates all three different reliability models. In some embodiments, the at least two different reliability models are continuously recalculated (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.) based on newly received TOF data, e.g., newly generated TOF data acquired over time, or newly generated TOF curves including newly acquired TOF data points. In some embodiments, the signal modeling module 203 continuously evaluates the at least two calculated reliability models to identify when at least two reliability models independently meet a predetermined threshold criterion (step 404). As described herein, the signal modeling module 203 is used to determine whether TOF data is valid and meets a predetermined threshold criterion in real time (e.g., during diffusion of a fluid into a biological specimen or fixation of the biological specimen).

[0082] In some embodiments, the system 200 further includes a prediction module 204 adapted to predict an estimated time for optimal diffusion of the fluid into the biological specimen or to predict an estimated time to fixation based on data received from the signal modeling module 203 (step 405). Further downstream processing steps may then be applied to the biological specimen. Each of these modules, as well as the step of determining the diffusion time of the fixative into the biological specimen such that the biological specimen is optimally fixed, are further described herein.

[0083] Signal Acquisition Module

[0084] In some embodiments, system 200 includes a signal acquisition module 201. In some embodiments, signal acquisition module 201 is adapted to generate acoustic data or an acoustic dataset, such as, for example, after the biological specimen is immersed in a fluid, such as one or more fixative solutions. In some embodiments, the acoustic data is generated by (i) transmitting an acoustic signal such that the acoustic signal encounters the biological specimen immersed in a fluid (e.g., any fixative described herein or a fixative described in U.S. Patent Application Publication No. 2017 / 0336363, the disclosure of which is incorporated herein by reference in its entirety), and (ii) detecting the acoustic signal after it encounters the biological specimen. In some embodiments, the acoustic data is repeatedly and / or continuously acquired and / or generated (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.). In some embodiments, the acoustic data is repeatedly and / or continuously acquired and / or generated at a single point within the biological specimen. In other embodiments, acoustic data is repeatedly and / or continuously acquired and / or generated along multiple points within or along the biological specimen, e.g., two or more points, three or more points, four or more points, five or more points, six or more points, ten or more points, etc.

[0085] In some embodiments, the signal acquisition module 201 generates acoustic data by a frequency sweep. As used herein, the term "frequency sweep" refers to a series of acoustic waves transmitted at regular frequency intervals through a medium (e.g., a medium containing one or more fixatives) such that a first set of acoustic waves is emitted through the medium at a regular frequency for a first regular duration, and a subsequent set of acoustic waves is emitted at regular frequency intervals for subsequent durations. In some embodiments, the durations are of equal duration.

[0086] In some embodiments, the signal acquisition module 201 comprises one or more transmitters and receivers configured such that an acoustic signal generated by the transmitter is received by the receiver and converted into a computer-readable signal. In some embodiments, the signal acquisition module 201 comprises an ultrasonic transmitter and an ultrasonic receiver. As used herein, a "transmitter" is a device capable of converting an electrical signal into acoustic energy, and an "ultrasonic transmitter" is a device capable of converting an electrical signal into ultrasonic acoustic energy. As used herein, a "receiver" is a device capable of converting acoustic waves into an electrical signal, and an "ultrasonic receiver" is a device capable of converting ultrasonic acoustic energy into an electrical signal.

[0087] Certain materials useful for generating acoustic energy from electrical signals are also useful for generating electrical signals from acoustic energy. Thus, the transmitter and receiver are not necessarily separate components, but may be. In some embodiments, the transmitter and receiver are configured such that the receiver detects acoustic waves generated by the transmitter after the transmitted waves encounter a material of interest, e.g., a biological analyte. In some embodiments, the receiver is configured to detect acoustic waves reflected by the material of interest, e.g., a biological analyte. In other embodiments, the receiver is configured to detect acoustic waves transmitted through the material of interest, e.g., a biological analyte. In some embodiments, at least two sets of transmitters and receivers are provided, where at least one of the at least two sets is positioned to transmit acoustic signals through a fluid (e.g., a fixative solution) and the biological analyte, and at least a second set is positioned to transmit acoustic signals through the fluid (e.g., a fixative solution) but not through the biological analyte. In this embodiment, the first set is used to measure TOF changes of the biological analyte, and the second set is used to detect TOF changes through the fluid (e.g., a fixative solution) (e.g., changes due to environmental variations such as temperature).

[0088] In some embodiments, the transmitter comprises a waveform generator operably coupled to the transducer, the waveform generator generating an electrical signal that is communicated to the transducer, which converts the electrical signal into an acoustic signal. In certain embodiments, the waveform generator is programmable, allowing a user to change certain parameters of the frequency sweep, including, for example, the start and / or end frequencies, the step size between frequencies in the frequency sweep, the number of frequency steps, and / or the duration for which each frequency is transmitted. In other embodiments, the waveform generator is preprogrammed to generate one or more predetermined frequency sweep patterns. In other embodiments, the waveform generator may be adapted to transmit both preprogrammed and customized frequency sweeps. In some embodiments, the transmitter may also include a focusing element that allows the acoustic energy generated by the transducer to be predictably focused and directed to a specific area.

[0089] During operation, the transmitter transmits a frequency sweep over the medium, which is then detected by the receiver and converted into an acoustic dataset to be stored in a non-transitory computer-readable storage medium and / or transmitted to a signal analyzer for analysis. If the acoustic dataset (e.g., physical acoustic waves) includes data representing the phase difference between the transmitted and received acoustic waves, the acoustic monitoring system may also include a phase comparator that generates an electrical signal corresponding to the phase difference between the transmitted and received acoustic waves. In some embodiments, the acoustic monitoring system comprises a phase comparator communicatively linked to the transmitter and receiver. In embodiments where the output of the phase comparator is an analog signal, the signal acquisition module 201 may also include an analog-to-digital converter for converting the analog output of the phase comparator to a digital signal. In some embodiments, the digital signal may then be recorded, for example, in a non-transitory computer-readable medium (memory 201 or storage subsystem 240) or communicated directly to the signal processing module 202 for analysis.

[0090] In some embodiments, the acoustic data represents at least a portion of a frequency sweep that is detected after the frequency sweep encounters a biological sample. In some embodiments, the detected portion of the frequency sweep constitutes an acoustic wave reflected by the biological sample. In other embodiments, the detected portion of the frequency sweep constitutes an acoustic wave that has passed through the biological sample.

[0091] In some embodiments, acoustic data may be acquired by the signal acquisition module 201 at a single point within the biological specimen (e.g., at or near the geometric center of the tissue sample). In other embodiments, acoustic data may be captured at multiple locations within the biological specimen (e.g., at evenly spaced locations). In embodiments in which acoustic data is collected from multiple locations within the tissue sample, a device may be provided for translating the tissue sample relative to the transmitters and receivers or for translating the transmitters and receivers relative to the tissue sample such that the common focal points of the transmitters and receivers move to different locations on the biological specimen. In some embodiments, the signal acquisition module 201 may be equipped with multiple transmitters and receivers, each having a different common focal point, and each set capturing acoustic data at a different location within the biological specimen.

[0092] U.S. Patent Application Publication No. 2017 / 0284859 further describes a method for capturing acoustic data across multiple different locations and further describes a movable cassette holder for acquiring such acoustic data. For example, the "different locations" can be locations within or on the surface of a tissue sample. According to some embodiments, the sample can be positioned at different "sample locations" by relative motion of the biopsy capsule and the acoustic beam path. The relative motion can include stepwise or continuous movement of the receiver and / or transducer to "scan" over the sample. Alternatively, the cassette can be repositioned by the movable cassette holder. For example, to image all of the tissue in the cassette, the cassette holder can be sequentially raised vertically by approximately 1 mm, and a TOF value can be acquired at each new location. As used herein, "cassette" can refer to, for example, a container for a biopsy capsule or a tissue sample not contained within a biopsy capsule. Preferably, the cassette is designed and shaped so that it can be automatically selected and moved, e.g., raised and lowered, relative to the beam path of the ultrasound transmitter / receiver pair, and further has an opening that allows for the movement of liquid reagents into and out of the cassette, and thus into and out of the tissue sample held therein. Movement may be performed, for example, by a robotic arm or another automated, movable component of the device into which the cassette is loaded. In other embodiments, the cassette alone is used to contain the tissue sample, and the shape of the cassette can, at least in part, determine the shape of the tissue sample. For example, by placing a rectangular tissue block slightly thicker than the cassette depth into the cassette and closing the cassette lid, the tissue sample can be compressed and expanded to fill most of the cassette's interior space, thus converting it into a thinner piece with a greater height and width but a thickness roughly corresponding to the cassette's depth. The disclosure of U.S. Patent Application Publication No. 2017 / 0284859 is incorporated herein by reference in its entirety.

[0093] Still other containers for holding biological specimens during fluid diffusion or fixation are described in International Publication No. 2011 / 071727, the disclosure of which is incorporated herein by reference in its entirety. In some embodiments, the biological specimen is provided within a biopsy capsule. As used herein, a "biopsy capsule" refers to a container for, for example, a biopsy tissue sample. Typically, a biopsy capsule contains a mesh to hold the specimen and allow liquid reagents, such as buffers, fixative solutions, or staining solutions, to surround and diffuse around the tissue specimen. The biopsy capsule can maintain the specimen in a particular shape that is computationally easy to model according to the disclosed methods and can therefore advantageously provide the specimen with a shape more suitable for use in the disclosed systems.

[0094] In some embodiments, the fixative solution may be maintained at a specific temperature or within a specific temperature range during at least a portion of the diffusion process (such as during a two-temperature fixation process, as described in more detail below). In some embodiments, the device for maintaining a quantity of fixative may be adapted to maintain the fixative solution at a specific temperature or within a specific temperature range. In some embodiments, the device may be insulated to substantially reduce heat transfer between the fixative solution and the surrounding environment. In some embodiments, the device may be configured with a heating or cooling device designed to maintain the fixative solution in which the tissue sample is immersed at a specific temperature or within a specific temperature range.

[0095] In some embodiments, systems and methods incorporate a two-temperature immersion fixation method for biological specimens. As used herein, "two-temperature fixation" refers to a fixation method in which tissue is first immersed in a cold fixative solution for a first period of time, followed by heating (passively or actively) the biological specimen for a second period of time. The "cold" diffusion step allows the fixative solution to diffuse throughout the tissue without substantially causing cross-linking. In some embodiments, the temperature of the fixative solution is maintained at a low temperature at least long enough to ensure that the fixative solution has diffused throughout the tissue sample. In some embodiments, the minimum amount of time to allow diffusion can be empirically determined by using various combinations of time and temperature in the cold fixative and evaluating the resulting tissue sample, looking at factors such as preservation of tissue structure and loss due to preservation of target analytes by immunohistochemistry (e.g., if the analyte is a protein or phosphorylated protein) or in situ hybridization (if the target analyte is a nucleic acid such as miRNA or mRNA). Alternatively, the minimum amount of time allowed for diffusion can be determined by monitoring diffusion using, for example, the methods outlined in Bauer et al., Dynamic Subnanosecond Time-of-Flight Detection for Ultra-precise Diffusion Monitoring and Optimization of Biomarker Preservation, Proceedings of SPIE, Vol. 9040, 90400B-1 (March 20, 2014).

[0096] In some embodiments, the systems and methods described herein can be utilized to estimate the time for optimal diffusion of a cold fixative throughout a biological specimen (such as the center of the biological specimen) (prior to fixation at a relatively high temperature, e.g., at least room temperature). Then, once the fixative has sufficiently diffused throughout the tissue, a heating step (or allowing the biological specimen and / or fluid to warm to room temperature) results in crosslinking by the fixative. In some embodiments, the systems and methods described herein can be utilized to determine the time to fixation during this second step of the two-temperature immersion fixation method.

[0097] The combination of cryodiffusion followed by a heating step (passive or active heating) results in a more completely fixed tissue sample than using standard methods. Thus, in embodiments, the tissue sample is fixed by: (1) monitoring the diffusion of fixative into the tissue sample by immersing the unfixed tissue sample in a cryofixative solution and monitoring the TOF in the tissue sample using the systems and methods disclosed herein (diffusion step), and (2) allowing the temperature of the tissue sample to be increased after a threshold TOF is measured (fixation step). In some embodiments, the diffusion step is performed in a fixative solution that is below about 20° C., below about 15° C., below about 12° C., below about 10° C., below about 8° C., below about 6° C., below about 4° C., in the range of about 0° C. to about 10° C., in the range of about 0° C. to about 12° C., in the range of about 0° C. to about 15° C., in the range of about 2° C. to about 10° C., in the range of about 2° C. to about 12° C., in the range of about 2° C. to about 15° C., in the range of about 5° C. to about 10° C., in the range of about 5° C. to about 12° C., in the range of about 5° C. to about 15° C. In some embodiments, the diffusion step may be performed at any of the above temperatures, and the sample may be stored in the fixative at a temperature below about 20° C., e.g., below about 15° C., or e.g., below about 10° C., for up to about 72 hours. In other embodiments, the temperature of the fixative solution surrounding the tissue sample is allowed to increase during the fixation step within a range of about 20° C. to about 55° C., such as within a range of about 20° C. to about 50° C., such as within a range of about 20° C. to about 45° C., such as within a range of about 20° C. to about 40° C., such as within a range of about 250° C. to about 55° C., such as within a range of about 25° C. to about 50° C., such as within a range of about 25° C. to about 45° C., or such as within a range of about 25° C. to about 50° C. Methods of fixation of biological specimens, including methods incorporating a two-temperature fixation protocol, are further described in U.S. Patent Application Publication No. 2012 / 0214195, the disclosure of which is incorporated herein by reference in its entirety.

[0098] In some embodiments, the two-temperature fixation process is particularly useful for methods of detecting certain labile biomarkers in tissue samples containing, for example, phosphorylated proteins, DNA, and RNA molecules (such as miRNA and mRNA). See PCT / EP2012 / 052800 (incorporated herein by reference). Accordingly, in certain embodiments, fixed tissue samples obtained using these methods can be analyzed for the presence of such labile markers. In some embodiments, methods are provided for detecting labile markers in a sample, comprising fixing the tissue according to the two-temperature fixation disclosed herein and contacting the fixed tissue sample with an analyte-binding entity capable of specifically binding to the labile marker, e.g., FOXP3. Examples of analyte-binding entities include antibodies and antibody fragments (including single-chain antibodies) that bind to target antigens, T cell receptors (including single-chain receptors) that bind to NHC:antigen complexes, MHC:peptide multimers (that bind to specific T cell receptors), aptamers that bind to specific nucleic acid or peptide targets, zinc fingers that bind to specific nucleic acids, peptides, and other molecules, receptor complexes (including single-chain receptors and chimeric receptors) that bind to receptor ligands, receptor ligands that bind to receptor complexes, and nucleic acid probes that hybridize to specific nucleic acids. For example, an immunohistochemical method for detecting phosphorylated proteins in a tissue sample is provided, which includes contacting fixed tissue obtained according to the above-described two-temperature fixation method with an antibody specific to the phosphorylated protein and detecting binding of the antibody to the phosphorylated protein. In some embodiments, an in situ hybridization method for detecting nucleic acid molecules is provided, which includes contacting fixed tissue obtained according to the above-described two-temperature fixation method with a nucleic acid probe specific to the nucleic acid of interest and detecting binding of the probe to the nucleic acid of interest.

[0099] Signal Processing Module

[0100] The system 200 of the present disclosure further includes a signal processing module 202. In some embodiments, the signal processing module 202 is part of the signal acquisition module 201. In other embodiments, the signal processing module 202 is separate from the signal acquisition module 201.

[0101] In either embodiment, the signal processing module 202 receives acoustic data from the signal acquisition module 201 (or from the memory 201 or storage subsystem 240 in communication therewith) and processes the received acoustic data to generate a TOF data point, one or more TOF curves, and / or one or more decay constants (collectively referred to herein as "TOF data"). In some embodiments, the acoustic data is generated continuously (e.g., over a predetermined time interval), and the signal processing module 202 processes the acoustic data as it is received, thereby providing TOF data as the acoustic data is continuously acquired. In some embodiments, the signal processing module 202 processes the acoustic data received from the signal acquisition module 201 in real time.

[0102] In some embodiments, the signal processing module 202 utilizes the retrieved acoustic data as input to calculate one or more TOF data points. In some embodiments, a "TOF data point" is the TOF difference between the bulk fluid and the biological specimen at a point in time. In some embodiments, each collected TOF data point represents (i) the calculated transit time difference of the acoustic wave between the calculated absolute transit times for the acoustic wave to travel through the fluid, and (ii) the calculated absolute transit time for the acoustic wave to pass through both the fluid and the biological specimen, as further described herein below.

[0103] In other embodiments, the signal processing module 202 utilizes the extracted acoustic data and / or the calculated TOF data points as input to calculate a "TOF curve" comprising a vector of "TOF data points" collected over time. In some embodiments, the TOF curve represents the diffusion of exogenous fluid into the biological specimen over time and up to a particular point in time (i.e., when the last TOF data point is collected). As more TOF data points are collected over time, a new TOF curve comprising the "new" TOF data points can be calculated. In some embodiments, the signal processing module 202 includes instructions for fitting the derived TOF curve to a single exponential curve.

[0104] In yet another embodiment, the signal processing module 202 calculates the decay constant or average decay constant (τ avg ) The operations performed by the signal processing module 202 are described further herein.

[0105] In some embodiments, one or more TOF data points, one or more TOF curves, and / or one or more calculated decay constants are provided as output to signal modeling module 203 and / or prediction module 204. In some embodiments, one or more TOF data points, one or more TOF curves, and / or one or more calculated decay constants are stored in memory 201 or storage subsystem 240 so that they can be retrieved as input by either signal modeling module 203 or prediction module 204.

[0106] Received acoustic data

[0107] In some embodiments, the signal processing module 202 utilizes the retrieved acoustic data to calculate the absolute amount of time it takes for an acoustic wave to travel between transducers in the signal acquisition module 201 (the "absolute transit time"). In some embodiments, the absolute transit time is the absolute transit time of an acoustic wave traveling through a fluid, measured between a transmitter and a receiver. In other embodiments, the absolute transit time is the absolute transit time of an acoustic wave traveling through a fluid and a biological specimen (e.g., a biological specimen placed in a histology cassette), measured between a transmitter and a receiver.

[0108] In some embodiments, the signal processing module 202 further utilizes the calculated absolute transit time to calculate the speed at which the acoustic wave travels through the fluid alone, as opposed to through the fluid and the biological sample. In these embodiments, a transit time difference is calculated between (i) the calculated absolute transit time for the acoustic wave to travel through the fluid and (ii) the calculated absolute transit time for the acoustic wave to travel through the fluid and the biological sample. In some embodiments, this process is repeated over time. In other embodiments, this process is repeated continuously as new data is acquired by the signal acquisition module 201, such that the signal processing module 202 calculates the data received from the signal acquisition module 201 in real time. In some embodiments, new data is received from the signal acquisition module 201 at least once every 0.5 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every second. In some embodiments, new data is received from the signal acquisition module 201 at least once every 1.5 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 2 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 5 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 10 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 15 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 20 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 30 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 40 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 50 seconds. In some embodiments, new data is received from the signal acquisition module 201 at least once every 60 seconds.

[0109] In some embodiments, the calculated difference over time represents a change in TOF. In some embodiments, changes in the biological specimen over time (e.g., the state of fixation of the biological specimen, such as when the biological specimen is fixed upon exposure to a solution containing one or more fixatives) may alter the calculated difference. For example, acoustic waves may travel faster through the biological specimen over time as it is fixed within a fluid containing one or more fixatives. Based on this, and in some embodiments, the calculated difference over time may help predict the fixation state of the biological specimen, changes in fixation state over time, or may be used to predict the amount of time fixation must continue before the biological specimen is adequately fixed.

[0110] TOF data point calculation

[0111] In some embodiments, the signal processing module 202 is used to calculate one or more TOF data points. In some embodiments, the TOF is estimated by the signal processing module 202 by comparing the phase of the transmitted and received acoustic waves. In some embodiments, an experimental frequency sweep is transmitted by a transmitter through a medium and detected by a receiver. In some embodiments, the phase of the transmitted and received waves is compared and converted to a temporal phase shift. In some embodiments, a simulation is then performed to model candidate temporal phase shifts at various candidate TOFs, and the error between the candidate temporal phase shift and the experimental temporal phase shift is generated and plotted as an error function. In some embodiments, the TOF that results in a minimum value of the error function is selected as the “observed” TOF. In some embodiments, the TOF is calculated by recording the transmitted phase shift between the transmitted and received ultrasound signals and fitting the recorded phase shift to multiple simulated phase shifts at different candidate TOFs.

[0112] In some embodiments, TOF is calculated using (i) an envelope method of calculating TOF, (ii) a linear regression method of calculating TOF, or (iii) a curve fitting method of calculating TOF. Each of the envelope method of calculating TOF, the linear regression method of calculating TOF, and the curve fitting method of calculating TOF are described below and further described in WO 2016 / 097164, the disclosure of which is incorporated herein by reference in its entirety.

[0113] Envelope method for TOF calculation

[0114] In the envelope method of TOF calculation, the TOF is based on the envelope of the minimum of a calculated error function. To calculate the envelope, an error function must first be generated. Generating the error function generally requires a comparison between: (1) a temporal phase shift generated from a recorded frequency sweep, and (2) multiple candidate temporal phase shifts simulated based on multiple candidate TOFs. This is repeated for each frequency in the frequency sweep. The error between the observed temporal phase shift and each candidate temporal phase shift is calculated and plotted as an error function. The envelope function is then applied to the error function. The minimum of the envelope function is selected as the observed TOF.

[0115] Linear regression method for TOF calculation

[0116] The linear regression method for calculating TOF involves calculating the TOF signal for each linear section of the ultrasound frequency sweep, performing a linear regression on each region, and averaging the slopes of each region to determine the true TOF. This may be in contrast to the previous embodiment, in which the slope of the phase frequency sweep is calculated directly, where the ideal phase of the frequency sweep is determined to find the ideal frequency, and the slope of that frequency is reconstructed to equal the TOF.

[0117] Curve fitting method for TOF calculation

[0118] The "curve fitting" method for calculating TOF takes advantage of the linearity of cumulative phase comparison with a frequency sweep. The TOF between two ultrasound transducers can be calculated by the slope of the phase-frequency curve obtained for the frequency sweep. However, after a full cycle is accumulated, the phase returns to zero, and therefore, the phase versus ultrasound frequency appears like a triangle wave. The algorithm generates a candidate triangle wave with a given amplitude, frequency, and phase. The amplitude, frequency, and phase of the candidate triangle wave are modified and compared to the experimentally detected triangle wave from the frequency sweep. The closest match between the candidate wave and the experimental wave is then used to directly calculate the observed TOF using the known relationship between the triangle wave's frequency and the absolute value of its slope. The slope is then used to calculate the TOF.

[0119] As an example of TOF calculation, Applicant has developed a method capable of robustly detecting sub-nanosecond TOF values in tissue samples immersed in fluid, e.g., one or more fixatives. In some embodiments, a transmit transducer, programmed by a programmable waveform generator, transmits a 3.7 MHz sinusoidal signal for 600 μs. In some embodiments, the pulse train is detected by a receive transducer after traversing the fluid and tissue, and the received and transmitted ultrasonic sinusoidal waves are then electronically compared with a digital phase comparator. In some embodiments, the output of the phase comparator is queried by an analog-to-digital converter, and the average value is recorded. In some embodiments, the process is repeated at multiple acoustic frequencies (ν). In some embodiments, given the transducer's center frequency (approximately 4.0 MHz) and fractional bandwidth (approximately 60%), a typical sweep ranges from approximately 3.7 to approximately 4.3 MHz, and the phase comparator is queried approximately every 600 Hz. In some embodiments, the voltage from the phase comparator is quantized by an experimentally determined phase (φ exp) into a temporal phase shift called . A brute force simulation is then used to calculate what the observed phase frequency sweep looks like for different TOF values. In some embodiments, the candidate temporal phase values as a function of the input sinusoidal frequency are calculated according to Equation 1: TIFF0007720412000003.tif18105

[0120] Here, TOF cand is the candidate TOF value in nanoseconds, T is the period of the input sine wave in nanoseconds, rnd represents rounding to the nearest integer function, and |...| is the absolute value symbol. In some embodiments, for a given candidate TOF and frequency value (i.e., period), the term on the right represents the time it takes for the nearest number of cycles to occur. In some embodiments, this value is expressed as the TOF cand to calculate the time phase to, or up to, the next complete cycle. Thus, in some embodiments, phase values are calculated for multiple candidate TOF values, initially in the range of 10-30 μs, spaced 200 ps apart. In some embodiments, the error between the experimental frequency sweep and the candidate frequency sweep is calculated in a least-squares sense for each candidate TOF value by Equation 2: JPEG0007720412000004.jpg15104

[0121] where N is the total number of frequencies in the sweep. In some embodiments, the normalized error function as a function of candidate TOF resembles an optical interferogram. For example, each feature has a width of one acoustic period (T = 1 / 4 MHz = 250 ns). A maximum error function indicates that the candidate phase-frequency sweep has an equal wavelength but is out of phase with the experimental phase-frequency sweep. Conversely, when the error is minimized, the two are perfectly matched, and thus the reconstructed TOF registers as the global minimum of the error function according to Equation 3: JPEG0007720412000005.jpg1472

[0122] In some embodiments, this technique of digitally comparing acoustic waves provides high accuracy due to the sharpness of the central trough, resulting in very good agreement between candidate and experimental phase frequency sweeps.

[0123] In some embodiments, the TOF data points are calculated continuously (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.) as new acoustic data is received and processed by the signal processing module 202. In some embodiments, the TOF data points are stored in memory 201 and / or a storage subsystem 240 in communication with the signal processing module 202. In some embodiments, the TOF data points or TOF curves are provided as input to the signal modeling module 203 and / or the prediction module 204.

[0124] Diffusion rate and decay constant calculations

[0125] In some embodiments, the signal processing module 202 is adapted to calculate the diffusion rate and / or the decay constant. In some embodiments, the calculated decay constant is provided as an input to the signal modeling module 203 and / or the prediction module 204. In some embodiments, to calculate the diffusion rate, the TOF curve is fitted to a single exponential curve to derive the TOF decay amplitude (A) and decay constant (τ). In some embodiments, the single exponential curve is of the form according to equation (4): TIFF0007720412000006.tif1171

[0126] where C is a constant offset, A is the amplitude of decay (i.e., the TOF value difference between a non-diffusing tissue sample and a fully diffusing tissue sample), τ is the decay constant, t is the diffusion time, and r is the spatial dependence (explicitly stated). In some embodiments, the constant offset C represents the TOF difference between the tissue sample and the bulk solution (e.g., tissue fluid or sample buffer). In some embodiments, the constant C can be set to 0 for visualization purposes.

[0127] In embodiments where acoustic data is collected at multiple spatial locations within or along a biological specimen, a spatially averaged TOF curve (i.e., a single curve representing the TOF at multiple spatial locations within the sample) is calculated and the average TOF amplitude (A) is determined by fitting the spatially averaged TOF curve to a single exponential curve. avg ) and the average decay constant (τ avg ) In some embodiments, the spatially averaged TOF curve comprises an equation according to equation (5): JPEG0007720412000007.jpg1899

[0128] Where TOF avg is the spatially averaged TOF curve, N is the number of spatial positions at which TOF curves are acquired, and C avg is the average constant offset, and A avg is the mean amplitude of the attenuation (i.e., the mean TOF value difference between non-diffusing and fully diffusing tissue samples), and τ avg is the average decay constant. In this context, "average" means the "spatial average" derived from data values obtained for a particular shared time point at different points in the sample.

[0129] In some embodiments, the diffusion rate at time t is calculated as the derivative of a single exponential curve at time t. In some embodiments, the diffusion rate of a non-spatially averaged TOF curve is calculated according to equation (6): TIFF0007720412000008.tif1658

[0130] where A is the amplitude of decay (i.e., the TOF value difference between a non-diffusing and a fully diffusing tissue sample), τ is the decay constant, and t is the diffusion time. In some embodiments, the diffusion rate of a spatially averaged TOF curve is calculated according to equation (7): TIFF0007720412000009.tif1761

[0131] In the formula, A avg is the mean amplitude of the decay (i.e., the spatial average of the TOF difference between a non-diffusing and a fully diffusing tissue sample), and τ avg is the average decay constant, and t is the diffusion time. In some embodiments, the diffusion rate can be calculated by taking the derivative of the curve as the amplitude (A or A) of the sample at time t. avg ) as the amplitude-normalized diffusion velocity. In some embodiments, the amplitude-normalized diffusion velocity is calculated for a non-spatially averaged TOF curve according to equation (8): JPEG0007720412000010.jpg2197

[0132] where τ is the decay constant, t is the diffusion time, the brackets indicate units of diffusion rate, and time is in units of time in terms of τ. In some embodiments, the amplitude-normalized diffusion rate is calculated for the spatially averaged TOF curve according to equation (9): JPEG0007720412000011.jpg2297

[0133] In the formula, τ avg is the average decay constant, t0 is the diffusion time, the brackets indicate the units of the diffusion rate, and time is τ avg The unit of time is

[0134] In some embodiments, the decay constants are calculated continuously (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.) as new acoustic data is received and processed by the signal processing module 202. In some embodiments, the calculated decay constants are stored in memory 205 and / or a storage subsystem 240 in communication with the signal processing module 202. In some embodiments, the calculated decay constants, or averages thereof, are provided as inputs to the signal modeling module 203 and / or the prediction module 204.

[0135] Further methods for deriving TOF data are described in U.S. Patent Application Publication Nos. 2017 / 0284920, 2017 / 0284859, and 2017 / 0284969, the disclosures of which are incorporated herein by reference in their entireties.

[0136] Signal Modeling Module

[0137] System 200 also includes a signal modeling module 203 for evaluating whether the measured TOF signal accurately reflects the actual diffusion rate of a fluid (e.g., one or more fixatives) through the biological specimen. In some embodiments, signal modeling module 203 receives TOF data (e.g., a predetermined number of TOF data points, a derived TOF curve, and / or a calculated decay constant) from signal processing module 202 or a memory 205 or storage subsystem 240 in communication therewith and calculates at least two different confidence models (step 403). In some embodiments, the calculated at least two different confidence models are a historical confidence model and a current confidence model, as described herein. In other embodiments, the calculated at least two different confidence models are a historical confidence model and a future confidence model. In other embodiments, the calculated at least two different confidence models are a current confidence model and a future confidence model. In other embodiments, the calculated at least two different confidence models are three different confidence models (step 403). In some embodiments, the three calculated different confidence models include a historical confidence model, a current confidence model, and a future confidence model, as described herein.

[0138] In some embodiments, once at least two confidence models are calculated (step 403), each of the at least two confidence models is independently compared to a predetermined threshold criterion to determine whether the confidence models are each independently "satisfied" (step 404). In some embodiments, once at least two confidence models are calculated and at least two confidence models are independently and / or simultaneously satisfied (step 404), a prediction module 204 is used to estimate the optimal diffusion time of the fluid through the biological specimen or the time to fixation (step 405). In some embodiments, the optimal diffusion time of the fluid through the biological specimen or the time to fixation is estimated using TOF data corresponding to the time points at which at least two confidence models were independently and / or simultaneously satisfied.

[0139] In other embodiments, three confidence models are calculated (step 403), and once at least two of the three calculated confidence models are satisfied (step 404), the time for optimal diffusion of the fluid through the biological specimen or the time to fixation is estimated using the prediction module 204. In some embodiments, the time for optimal diffusion of the fluid through the biological specimen or the time to fixation is estimated using TOF data corresponding to the time points at which at least two confidence models (of the three calculated confidence models) were satisfied independently and / or simultaneously.

[0140] In yet other embodiments, three confidence models are calculated (step 403), and once all three confidence models are satisfied independently and / or simultaneously (step 404), the prediction module 204 is used to estimate the optimal diffusion time of the fluid through the biological specimen or the time to fixation (step 405). In some embodiments, the optimal diffusion time of the fluid through the biological specimen or the time to fixation is estimated using the TOF data corresponding to the time at which the three confidence models were satisfied independently and / or simultaneously.

[0141] Examples of each of the different confidence models and predetermined threshold criteria for independently "satisfying" the different confidence models are further described herein. An exemplary time course showing past, present, and future confidence models and their predictive power is described in Example 1 and shown in Figures 5-9.

[0142] In some embodiments, after each new successive TOF data point derived by signal processing module 202 and received by signal modeling module 203, each reliability model is updated (recalculated) based on the newly received TOF data point. In some embodiments, at least two reliability models are continuously recalculated as new TOF data is received as input (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.). In other embodiments, each of the three reliability models is continuously recalculated as new TOF data is received as input (e.g., every 0.2 seconds, 0.5 seconds, 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 60 seconds, etc.).

[0143] In some embodiments, new TOF data is received at least once every 0.2 seconds, after which the respective reliability models are recalculated using the newly received TOF data (e.g., using the newly received TOF data points). In some embodiments, new TOF data is received at least once every 0.5 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 0.7 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 1 second, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 1.5 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 2 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 3 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 4 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 5 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 10 seconds, after which the respective reliability models are recalculated using the newly received TOF data.

[0144] In some embodiments, new TOF data is received at least once every 15 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 20 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 25 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 30 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 35 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 40 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 45 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 50 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 55 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 60 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 65 seconds, after which the respective reliability models are recalculated using the newly received TOF data.

[0145] In some embodiments, new TOF data is received at least once every 70 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 80 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 90 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 100 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 110 seconds, after which the respective reliability models are recalculated using the newly received TOF data. In some embodiments, new TOF data is received at least once every 120 seconds, after which the respective reliability models are recalculated using the newly received TOF data.

[0146] Past reliability models

[0147] The historical confidence model determines whether the measured diffusion rates of one or more fluids (including one or more fixatives) through the biological specimen were consistently resolved by the TOF system. In other words, the historical confidence model is used to establish that a candidate TOF data point matches one or more previously collected TOF data points. For example, the historical confidence model can be used to establish that a candidate TOF data point recorded in time interval 12 matches collected TOF data points recorded in each of time intervals 11, 10, 9, 8, 7, and 6.

[0148] Based on Fick's first law, it is expected that the diffusion of a fluid (including one or more fixatives) through a biological specimen can be described by a single parameter, i.e., a diffusion constant. In some embodiments, the diffusion of a biological specimen can be characterized by a single exponential decay governed by a calculated decay constant (e.g., see the decay constant calculated using the signal processing module 202). In some embodiments, if the calculated decay constant changes significantly over time (e.g., for consecutively received TOF data points received over time), it indicates that the diffusion profile is unable to converge to the "true" profile. In other words, in the context of fixation of a biological specimen, a wide variation in consecutively calculated decay constants calculated over time (e.g., over a predetermined number of consecutively received TOF data points) indicates that the calculated diffusion rate does not yet represent the true diffusion rate of the fixative through the biological specimen. On the other hand, a relatively consistent decay constant over time (e.g., over a predetermined number of consecutively received TOF data points) indicates that the calculated diffusion rate represents the true diffusion rate of the fixative through the biological specimen.

[0149] In some embodiments, the historical confidence model takes as input one or more derived TOF data points and / or one or more decay constants from the signal processing module 202 or the memory 201 or storage subsystem 240 in communication therewith, and determines whether the measured diffusion rate is consistently resolved within a predetermined threshold margin over a predetermined number of consecutively derived TOF data points. In some embodiments, the measured diffusion rate must be consistently resolved within the predetermined threshold margin for three consecutively derived TOF data points. In other embodiments, the measured diffusion rate must be consistently resolved within the predetermined threshold margin for four consecutively derived TOF data points. In yet other embodiments, the measured diffusion rate must be consistently resolved within the predetermined threshold margin for five consecutively derived TOF data points. In a further embodiment, the measured diffusion rate must be consistently resolved within the predetermined threshold margin for six consecutively derived TOF data points. In yet a further embodiment, the measured diffusion rate must be consistently resolved within the predetermined threshold margin for seven consecutively derived TOF data points. In still further embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for eight consecutively derived TOF data points. In still further embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for nine consecutively derived TOF data points. In other embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for ten consecutively derived TOF data points. In yet other embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for twelve consecutively derived TOF data points. In a further embodiment, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for fifteen consecutively derived TOF data points.In still further embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for 18 consecutively derived TOF data points. In yet further embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for 21 consecutively derived TOF data points. In still further embodiments, the measured diffusion rates must be consistently resolved within a predetermined threshold margin for 24 consecutively derived TOF data points.

[0150] In some embodiments, a calculated historical reliability model is satisfied (i.e., meets a predetermined historical reliability model threshold criterion) when a predetermined number of retrieved calculated candidate decay constants corresponding to a plurality of consecutively derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average decay constant. In some embodiments, one or more convergence testing algorithms are used to determine whether the calculated candidate decay constants fall within or meet the predetermined historical reliability model threshold criterion. In some embodiments, the convergence testing algorithms are selected from absolute convergence, alternating series test, direct comparison test, integral test, marginal comparison test, p-series convergence, ratio test, root test, Cauchy condensation test, Abel test, Dirichlet test, Rabe-Duhamel test, Bertrand test, and Gauss test.

[0151] In some embodiments, the convergence test includes (i) calculating a candidate decay constant for the candidate TOF data point to provide a "theoretical value," (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an "experimental value," (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value, and (iv) comparing the determined percent error to a predetermined threshold percentage value. In some embodiments, the actual percent error is calculated according to equation (10): Error % = ((calculated theoretical value - calculated experimental value) / calculated theoretical value) x 100(10)

[0152] In some embodiments, the average decay constant is derived by: (i) retrieving from memory 202 or storage subsystem 240 the calculated decay constant for each of a predetermined number of consecutively derived TOF data points preceding the candidate TOF data point; and (ii) averaging each of the retrieved calculated decay constants. In some embodiments, the candidate TOF data point is the last received derived TOF data point. The average decay constant is based on the calculated decay constants for the predetermined number of consecutively derived TOF data points preceding the candidate TOF data point. For example, the candidate TOF data point may be in time interval 10, and the average decay constant is calculated based on TOF data points retrieved in time intervals 9, 8, 7, 6, and 5.

[0153] In some embodiments, the average decay constant is calculated based on at least two TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least three TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least four TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least five TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least six TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least seven TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least eight TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least nine TOF data points preceding the candidate TOF data point. In other embodiments, the average decay constant is calculated based on at least ten TOF data points preceding the candidate TOF data point.

[0154] In some embodiments, the predetermined threshold percentage value is less than about 5%. In other embodiments, the predetermined threshold percentage value is less than about 4%. In still other embodiments, the predetermined threshold percentage value is less than about 3%. In some embodiments, the predetermined threshold percentage value is between about 1% and about 3%. In other embodiments, the predetermined threshold percentage value is between about 1.5% and about 3%. In still other embodiments, the predetermined threshold percentage value is between about 1.75% and about 2.5%. In further embodiments, the predetermined threshold percentage value is between about 1.75% and about 2.25%. In some embodiments, the predetermined threshold percentage value is about 2%.

[0155] In some embodiments, the historical confidence model is satisfied when at least five calculated candidate decay constants corresponding to at least five consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant. In other embodiments, the historical confidence model is satisfied when at least six calculated candidate decay constants corresponding to at least six consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant. In other embodiments, the historical confidence model is satisfied when at least seven calculated candidate decay constants corresponding to at least seven consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant. In other embodiments, the historical confidence model is satisfied when at least eight calculated candidate decay constants corresponding to at least eight consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant. In another embodiment, the historical confidence model is satisfied when at least nine calculated candidate decay constants corresponding to at least nine consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant. In another embodiment, the historical confidence model is satisfied when at least ten calculated candidate decay constants corresponding to at least ten consecutively derived TOF data points are each within a predetermined threshold percentage value of the calculated average decay constant.

[0156] Current Reliability Model

[0157] The current confidence model is used to establish that there is a high degree of statistical confidence for predicting future TOF data points. While the historical confidence model determines whether the diffusivity over a predetermined number of consecutively derived TOF data points is consistent, the current confidence model establishes whether the current TOF data point is consistent with at least some of the previously collected TOF data, thus facilitating the elimination of false positives generated by the historical confidence model. For example, if the current confidence model repeatedly calculates the same decay constant but with low confidence, the current confidence model correctly determines that the signal was sufficiently valid to make a prediction.

[0158] In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 20% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 30% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 40% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 50% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 60% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 70% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 80% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 90% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 95% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 98% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches at least 99% of the previously collected TOF data. In some embodiments, the current confidence model establishes whether the current TOF data point matches all of the previously collected TOF data.

[0159] In some embodiments, the current confidence model (i) takes as input a derived TOF curve (including some or all TOF data points up to the point in time for which the current confidence model is being calculated) and / or one or more derived decay constants from signal processing module 202 or memory 201 or storage subsystem 240 in communication therewith, (ii) calculates a confidence interval based on the retrieved derived TOF curve and / or decay constants, and (iii) evaluates whether the calculated confidence interval is below a predetermined threshold. For example, the current confidence model may calculate a 95% confidence interval based on the derived decay constants corresponding to all TOF data points in the acquired TOF curve and evaluate whether the calculated confidence interval is below a predetermined current confidence model threshold.

[0160] In some embodiments, the confidence interval of the current confidence model is calculated by: (a) performing a nonlinear regression fit of the TOF curve using some or all of the acquired TOF data points of the TOF curve, and (b) calculating a confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting. In some embodiments, all of the calculated TOF data points are used to calculate the current confidence model. In other words, all of the calculated TOF data points up to the point at which the confidence interval is being calculated are used in the nonlinear regression fitting of the calculated TOF curve. The confidence interval of the decay constant is calculated from the fit. In some embodiments, the nonlinear fitting and confidence interval calculation may be determined using the procedures described at https: / / www.astro.rug.nl / software / kapteyn / kmpfittutorial.html.

[0161] In another embodiment, the "nlpredci.m" algorithm in matlab ("nonlinear regression prediction confidence intervals") is trained using the covariance matrix and mean squared error calculations.

[0162] In some embodiments, the current confidence model is continuously calculated after each new TOF data is derived, e.g., after each new TOF data point, TOF curve, and / or decay constant is derived, and the above fitting and statistical analysis are repeated as new TOF data is derived.

[0163] As described above, the calculated confidence interval is then compared to the current confidence model threshold to determine whether the condition is met. In some embodiments, the current confidence model threshold is in the range of about 0.1 hours to about 2 hours. In some embodiments, the current confidence model threshold is in the range of about 0.1 hours to about 1.8 hours. In some embodiments, the current confidence model threshold is in the range of about 0.1 hours to about 1.6 hours. In some embodiments, the current confidence model threshold is in the range of about 0.2 hours to about 1.4 hours. In other embodiments, the current confidence model threshold is in the range of about 0.3 hours to about 1.3 hours. In other embodiments, the current confidence model threshold is in the range of about 0.4 hours to about 1.1 hours. In other embodiments, the current confidence model threshold is in the range of about 0.5 hours to about 0.9 hours. In other embodiments, the current confidence model threshold is in the range of about 0.6 hours to about 0.8 hours. In yet other embodiments, the current confidence model threshold is about 0.7 hours.

[0164] Nonlinear regression is a regression in which a dependent or criterion variable is modeled as a nonlinear function of model parameters and one or more independent variables. In some embodiments, nonlinear regression analysis models trends as nonlinear functions, which may be, by way of non-limiting example, exponential, logarithmic, trigonometric, power series, or a combination of one or more of these functions. Specific parameters of the nonlinear function may be determined by "fitting" the plotted function, such as by applying a curve fitting technique (by way of non-limiting example, least squares) to minimize the residual between the plot and the nonlinear function. In some embodiments, the nonlinear regression is selected from an asymptotic regression / growth model, a logistic population growth model, or an asymptotic regression / decay model.

[0165] Future Reliability Model

[0166] The future confidence model calculates the confidence of the signal across the entire experiment, including signals retrieved in the past, present, and future. In this regard, the future confidence model analyzes how well the received TOF data points and / or TOF fits match the overall diffusion profile of the TOF signal. The future confidence model is unique from the past confidence model and the current confidence model because it takes into account the amplitude of diffusion and future predictive power. Both the past and current confidence models are adapted to look at the time profile of the TOF curve, which is defined by the decay constant. The decay constant (i.e., the diffusion rate) is independent of the amplitude or total amount of fluid exchange. The future model focuses on how confident the model is in predicting new TOF data points with respect to the diffusion rate and magnitude of diffusion.

[0167] Because the confidence level of already collected data tends to be high, the future confidence model is likely to be difficult to meet at the beginning of an experiment and tend to improve as the experiment progresses, i.e., as diffusion continues over time. Therefore, it provides a quality check for determining whether a TOF data point is valid too early in an experiment. For example, the future confidence model could be used to prevent the system from prematurely calling a received TOF data point valid if the erroneous decay rate continues to resolve. As a result, the future confidence model also likely improves the system's quality for unusually large samples, which may have significantly more fluid exchange than larger standard samples.

[0168] In some embodiments, the input to the future reliability model is a TOF curve retrieved from the signal processing module 202 or the memory 201 or storage subsystem 240 in communication therewith. In some embodiments, the future reliability model calculates a confidence interval based on the retrieved TOF curve and evaluates whether the mean of the calculated confidence interval is below a predetermined threshold. For example, in some embodiments, the confidence interval is calculated by: (a) performing a nonlinear regression fit of the TOF curve using all calculated TOF data points of the calculated TOF curve; (b) determining a confidence interval for the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point; and (c) calculating a mean confidence interval for the TOF curve. In some embodiments, the future reliability model may calculate a mean 95% confidence interval for the TOF curve over all times up to a predetermined amount of time, e.g., 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, etc. In some embodiments, nonlinear regression fitting (described above) may be utilized to derive the confidence interval based on the retrieved TOF curve.

[0169] In some embodiments, the predetermined future confidence model threshold is 0.7 ns. In some embodiments, the threshold is 0.6 ns. In other embodiments, the predetermined future confidence model threshold is 0.55 ns. In other embodiments, the predetermined future confidence model threshold is 0.5 ns. In other embodiments, the threshold is 0.45 ns. In other embodiments, the predetermined future confidence model threshold is 0.4 ns. In other embodiments, the predetermined future confidence model threshold is 0.35 ns. In other embodiments, the predetermined future confidence model threshold is 0.3 ns. In other embodiments, the predetermined future confidence model threshold is 0.25 ns. In other embodiments, the predetermined future confidence model threshold is 0.2 ns. In other embodiments, the predetermined future confidence model threshold is 0.15 ns. In other embodiments, the predetermined future confidence model threshold is 0.1 ns.

[0170] Prediction Module

[0171] In some embodiments, the system 200 further includes a prediction module 204 adapted to predict an estimated time for optimal fluid diffusion through the biological specimen or an estimated time to fixation based on data received from the signal modeling module 203. Following calculation of at least two reliability models (step 403) and determination that the calculated at least two reliability models each independently and / or simultaneously satisfy a predetermined threshold criterion (step 404), the prediction module 204 is used to calculate an estimated time for optimal fluid diffusion through the biological specimen or an estimated time to fixation completion (step 405). In some embodiments, the prediction module 204 utilizes TOF data at time points where at least two reliability models were independently and / or simultaneously satisfied. For example, if at least two reliability models were independently satisfied at the 2.5 hour time point, the prediction module 204 utilizes the TOF data (e.g., TOF data points, TOF curves, and / or decay constants) collected at the 2.5 hour time point, as this is the time point at which the TOF data is considered valid. In other words, when at least two confidence models independently and / or simultaneously meet predetermined threshold criteria in the context of fixation of a biological specimen, system 200 determines that the TOF signal from the biological specimen truly represents the actual diffusion rate at that point and predicts when the tissue is adequately fixed.

[0172] In some embodiments, an estimate of the optimal diffusion time of a fluid through a biological specimen or an estimate of the fixed time to completion is calculated using the TOF curves described herein. In some embodiments, the TOF curve is measured at a single location within the biological specimen (see Equation (11)). In other embodiments, the TOF curve is measured at multiple locations with the biological specimen (a spatially averaged TOF curve) (see Equation (12)).

[0173] In embodiments where the TOF curve is measured at a single location, the time for optimal diffusion of fluid into the biological specimen or time to complete immobilization is estimated according to equation (11): TIFF0007720412000012.tif1377

[0174] In the formula, t done is the estimated time to completion of diffusion or fixation, and the |...| symbol indicates an absolute value. In some embodiments, T done represents the predicted time (hours) for the tissue to be sufficiently diffused with the fixative.

[0175] In embodiments where a spatially averaged TOF curve is used, the time for optimal diffusion of fluid into the biological specimen or time to complete immobilization may be calculated according to equation (12): TIFF0007720412000013.tif1484

[0176] In the formula, t done is the time to completion of diffusion fixation (and represents the time in hours that the tissue is expected to be sufficiently diffused), and τ avg is the spatially averaged decay constant of the tissue, and m is the normalized threshold slope of the TOF curve ("threshold" means that when the slope decreases to the "threshold" value, diffusion slows down sufficiently and the tissue becomes sufficiently well diffused that our model predicts proper staining). In some embodiments, m thres is 0.074.

[0177] In some embodiments, in the context of fixation of a biological specimen, after a predicted time to fixation is determined, the biological specimen remains immersed in the fixative until the predicted fixation time is reached, assuming the predicted fixation time is in the future. On the other hand, if the predicted time to fixation is in the present or past, the biological specimen is removed from the fixative solution. Once the biological specimen is optimally fixed, it can be used in further downstream processes.

[0178] Labeling one or more biomarkers in a biological specimen

[0179] Once the biological specimen has been properly fixed, the biological specimen may be utilized in one or more downstream processes, such as antigen retrieval, staining, etc. In some embodiments, the biological specimen may be labeled for the presence of one or more biomarkers and / or counterstained once fixation is complete (or presumed to be complete). For example, the biological specimen may be subjected to fixation for the presence of one or more biomarkers, such as Ki-67 and p16. INK4a In some embodiments, a biological specimen stained for the presence of one or more biomarkers may be imaged and subsequently analyzed using one or more automated image analysis algorithms, including machine learning algorithms and / or "artificial intelligence."

[0180] Methods for labeling one or more biomarkers in a biological specimen are known in the art. In some embodiments, a detectable moiety (e.g., a hapten, chromophore, fluorophore, etc.) is deposited on or in proximity to a target in the biological specimen. In some embodiments, covalent attachment of the chromophore or detectable moiety is achieved using tyramide signal amplification (TSA), also known as catalytic reporter attachment (CARD). U.S. Pat. No. 5,583,001, the disclosure of which is incorporated herein by reference in its entirety, discloses a method for detecting and / or quantifying an analyte using an analyte-dependent enzyme activation system that relies on catalytic reporter attachment to amplify a detectable label signal. The catalytic action of the enzyme in the CARD or TSA method is enhanced by reacting a labeled phenol molecule with the enzyme. Modern methods utilizing TSAs effectively increase the signal obtained from IHC and ISH assays without introducing significant background signal amplification (see, e.g., U.S. Patent Application Publication No. 2012 / 0171668, which is incorporated by reference in its entirety for its disclosure regarding tyramide amplification reagents). Reagents for these amplification approaches have been applied to clinically important targets to provide previously unattainable robust diagnostic capabilities (VENTANA OptiView Amplification Kit, Ventana Medical Systems, Tucson, Arizona, Catalog No. 760-099).

[0181] TSA utilizes a reaction catalyzed by horseradish peroxidase (HRP) acting on tyramide. In the presence of H2O2, tyramide is converted to a highly reactive, short-lived radical intermediate that preferentially reacts with electron-rich amino acid residues on proteins. The covalently attached detectable moiety can then be detected by various chromogenic visualization techniques and / or fluorescence microscopy. In IHC and ISH, where spatial and morphological context are crucial, the short lifetime of the radical intermediate results in covalent attachment of tyramide to tissues proximal to the site of generation, thereby providing a discrete and specific signal at the location of protein and nucleic acid targets.

[0182] In other embodiments, covalent attachment of the chromophore or detectable moiety is achieved using quinone methide chemistry. U.S. Patent No. 10,168,336, issued January 1, 2019, entitled "Quinone Methide Analog Signal Amplification," the entire disclosure of which is incorporated herein by reference, describes a technique ("QMSA") that, like TSA, can be used to increase signal amplification without significantly increasing background signal. In particular, U.S. Patent No. 10,168,336 describes novel quinone methide analog precursors and methods of using the quinone methide analog precursors to detect one or more targets in a biological specimen. In certain embodiments, the detection method involves contacting a sample with a detection antibody or probe, and then contacting the sample with a labeled conjugate comprising an alkaline phosphatase (AP) enzyme and a binding moiety, where the binding moiety recognizes the antibody or probe (e.g., by binding to a hapten or a species-specific antibody epitope, or a combination thereof). The alkaline phosphatase enzyme of the label conjugate interacts with a quinone methide analog precursor containing a detectable moiety, thereby forming a reactive quinone methide analog, which is covalently attached to the biological specimen either proximal to the target or directly. The detectable label is then detected visually or by imaging techniques, etc. U.S. Patent No. 10,168,336 is incorporated herein by reference in its entirety.

[0183] Another technique for attaching detectable moieties uses "click" chemistry to form covalent bonds between detectable moieties and morphological or biomarkers in a sample. "Click chemistry" is a chemical philosophy originally defined by the Sharpless and Meldal groups, which together describe chemistry tailored to rapidly and reliably generate substances by linking small units. "Click chemistry" has been applied to a collection of reliable and autonomous organic reactions (Kolb, HC; Finn, MG; Sharpless, KB Angew. Chem. Int. Ed. 2001, 40, 2004-2021). In the context of covalently attaching detectable labels to biological specimens, click chemistry technology is described in U.S. Patent Application Publication No. 2019 / 0204330, which is incorporated herein by reference in its entirety. This technique uses either tyramide deposition, as described above, or quinone methide deposition, also described above, to covalently anchor a first reactive group, capable of participating in a click chemistry reaction, to the biological specimen. A second component of the detection system having a corresponding second reactive group capable of participating in a click chemistry reaction is then reacted with the first reactive group to covalently link the second component to the biological analyte.

[0184] In certain embodiments, the described techniques involve contacting a biological sample with a first detection probe specific for a first target. The first detection probe can be a primary antibody or a nucleic acid probe. The sample is then contacted with a first labeled conjugate containing a first enzyme. In some embodiments, the first labeled conjugate is a secondary antibody specific for either the primary antibody (e.g., the species from which the antibody was obtained) or a label (e.g., a hapten) conjugated to the nucleic acid probe. The biological sample is then contacted with a first member of a click conjugate pair. The first enzyme cleaves the first member of the click conjugate pair, which contains a tyramide or quinone methide precursor, thereby converting the first member into a reactive intermediate that covalently binds to the biological sample either proximal to the first target or directly. Next, the second member of the click conjugate pair is contacted with the biological sample, the second member of the click conjugate pair comprising a first reporter moiety (e.g., a chromophore) and a second reactive functional group, the second reactive functional group of the second member of the first click conjugate pair being capable of reacting with the first reactive functional group of the first member of the click conjugate pair, and finally, a signal from the first reporter moiety is detected.

[0185] Examples of biomarkers

[0186] As described herein, the fixation estimation engine is trained using a training spectral dataset obtained from multiple differently fixed training biological specimens. In some embodiments, the class labels of known fixation durations are validated by functional IHC testing. Identified below are non-limiting examples of biomarkers whose expression can be determined by functional IHC staining. Certain markers are specific cellular properties, while others are identified as being associated with specific diseases or conditions. Examples of known prognostic markers include enzyme markers such as galactosyltransferase II, neuron-specific enolase, proton ATPase-2, and acid phosphatase. Hormone or hormone receptor markers include human chorionic gonadotropin (HCG), adrenocorticotropic hormone, carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), estrogen receptor, progesterone receptor, androgen receptor, gC1q-R / p33 complement receptor, IL-2 receptor, p75 neurotrophin receptor, PTH receptor, thyroid hormone receptor, and insulin receptor.

[0187] Lymphoid markers include alpha-1-antichymotrypsin, alpha-1-antitrypsin, B cell markers, bcl-2, bcl-6, B lymphocyte antigen 36kD, BM1 (myeloid marker), BM2 (myeloid marker), galectin-3, granzyme B, HLA class I antigens, HLA class II (DP) antigens, HLA class II (DQ) antigens, HLA class II (DR) antigens, human neutrophil defensins, immunoglobulin A, immunoglobulin D, immunoglobulin G, immunoglobulin M, kappa light chains, kappa light chains, lambda light chains, lymphocyte / histiocytic antigens, macrophage markers, muramidase (lysozyme), p80 anaplastic lymphoma kinase, plasma cell markers, secretory leukocyte protease inhibitor, T cell antigen receptor (JOVI 1), and T cell antigen receptor (JOVI 2). 3), including termidonucleotidyl transferase, a marker of non-clustered B cells.

[0188] Tumor markers include alpha-fetoprotein, apolipoprotein D, BAG-1 (RAP46 protein), CA19-9 (sialyl Lewis), CA50 (carcinoma-associated mucin antigen), CA125 (ovarian cancer antigen), CA242 (tumor-associated mucin antigen), chromogranin A, clusterin (apolipoprotein J), epithelial membrane antigen, epithelial-associated antigen, epithelial-specific antigen, epidermal growth factor receptor, estrogen receptor (ER), macroscopic cystic disease fluid protein-15, hepatocyte-specific antigen, HER2, heregulin, human gastric mucin, human milk fat globule, MAGE-1, matrix metalloproteinase, melan A, melanoma marker (HMB45), mesothelin, metallothionein, microphthalmic transcription factor (MITF), Muc-1 core glycoprotein, and Muc-1 glycoprotein. Protein, Muc-2 glycoprotein, Muc-5AC glycoprotein, Muc-6 glycoprotein, myeloperoxidase, Myf-3 (rhabdomyosarcoma marker), Myf-4 (rhabdomyosarcoma marker), MyoD1 (rhabdomyosarcoma marker), myoglobin, nm23 protein, placental alkaline phosphatase, prealbumin, progesterone receptor, prostate-specific antigen, prostatic acid phosphatase, prostatic inhibin peptide, PTEN, renal cell carcinoma marker, small intestinal mucous antigen, tetranectin, thyroid transcription factor-1, tissue inhibitor of matrix metalloproteinase 1, tissue inhibitor of matrix metalloproteinase 2, tyrosinase, tyrosinase-related protein-1, villin, von Willebrand factor, CD34, CD34 class II, CD51Ab-1, CD63, CD69, Chk1, Chk2, Claspin C-met, COX6C, CREB, Cyclin D1, Cytokeratin, Cytokeratin 8, DAPI, Desmin, DHP (1-6 diphenyl-1,3,5-hexatriene), E-cadherin, EEA1, EGFR, EGFRvIII, EMA (epithelial membrane antigen), ER, ERB3, ERCC1, ERK, E-selectin, FAK, Fibronectin, FOXP3, gamma-H2AX, GB3, GFAP, Giantin, GM130, Golgin97, GRB2, GRP78BiP, GSK3 beta, HER-2, Histone 3, Histone 3K14-ace [anti-acetyl-histone H3 (Lys)] s14)], Histone 3_K18-Ace [Histone H3-acetyl Lys18), Histone 3_K27-TriMe, [Histone H3(trimethyl K27)], Histone 3_K4-diMe [Antidimethyl-Histone H3(Lys4)], Histone 3_K9-Ace [Acetyl-Histone H3(Lys9)], Histone 3_K9-triMe [Histone 3-trimethyl Lys9], Histone 3_S10-Phos [Anti-Phospho Histone H3(Ser10), mitotic marker], Histone 4, Histone H2A.X-5139-Phos [Phospho Histone H2A.X(Ser139) antibody], Histone H2B, Histone H3_DiMethyl K4, Histone H4_TriMethyl K20-Chip grad, HSP70, Urokinase, VEGF R1, ICAM-1, IGF-1, IGF-1R, IGF-1 receptor beta, IGF-II, IGF-IIR, IKB-Alpha IKKE, IL6, IL8, Integrin alpha V beta 3, Integrin alpha V beta 6, Integrin alpha V / CD51, Integrin B5, Integrin B6, Integrin B8, Integrin beta 1 (CD29), Integrin beta 3, Integrin beta 5 Integrin B6, IRS-1, Jagged1, Anti-protein kinase CBeta2, LAMP-1, light chain Ab-4 (cocktail), lambda light chain, kappa light chain, M6P, Mach2, MAPKAPK-2, MEK1, MEK1 / 2 (Ps222), MEK2, MEK1 / 2 (47E6), MEK1 / 2 blocking peptide, MET / HGFR, MGMT, mitochondrial antigen, Mitotracker Green FM, MMP-2, MMP9, E-cadherin, mTOR, ATPase, N-cadherin, nephrin, NFKB, NFKB p105 / p50, NF-KB p65, Notch1, Notch2, Notch3, OxPhos complex IV, p130Cas, p38 MAPK, p44 / 42 MAPK antibody, P504S, P53, P70, P70 S6K, pan-cadherin, paxillin, P-cadherin, PDI, pEGFR, phospho-AKT, phospho-CREB, phospho-EGF receptor, phospho-GSK3 beta, phospho-H3, phospho-HSP-70, phospho-MAPKAPK-2, phospho-MEK1 / 2, phospho-p38 MAP kinase, phospho-p44 / 42 MAPK, phospho-p53, phospho-PKC, phospho-S6 ribosomal protein, phospho-Src, phospho-Akt, phospho-Bad, phospho-IKB-a, phospho-mTOR, phospho-NF-kappaB P65, phospho-p38, phospho-p44 / 42 MAPK, phospho-p70 These include S6 kinase, Phospho-Rb, Phospho-Smad2, PIM1, PIM2, PKCβ, podocalyxin, PR, PTEN, R1, Rb 4H1, R-cadherin, ribonucleotide reductase, RRM1, RRM11, SLC7A5, NDRG, HTF9C, HTF9C, CEACAM, p33, S6 ribosomal protein, Src, survivin, sinapopodin, syndecan-4, talin, tensin, thymidylate synthase, tuberin, VCAM-1, VEGF, vimentin, agglutinin, YES, ZAP-70, and ZEB.

[0189] Cell cycle-related markers include apoptotic protease-activating factor-1, bcl-w, bcl-x, bromodeoxyuridine, CAK (cdk-activating kinase), cellular apoptosis-susceptible protein (CAS), caspase 2, caspase 8, CPP32 (caspase-3), CPP32 (caspase-3), cyclin-dependent kinase, cyclin A, cyclin B1, cyclin D1, cyclin D2, cyclin D3, cyclin E, cyclin G, DNA fragmentation factor (N-terminal), and Fa These proteins include CD95, Fas-associated death domain protein, Fas ligand, Fen-1, IPO-38, Mc1-1, minichromosome maintenance protein, mismatch repair protein (MSH2), poly(ADP-ribose) polymerase, proliferating cell nuclear antigen, p16 protein, p27 protein, p34cdc2, p57 protein (Kip2), p105 protein, Stat1α, topoisomerase I, topoisomerase IIα, topoisomerase IIIα, and topoisomerase IIβ.

[0190] Neural tissue and tumor markers include alpha B-crystallin, alpha-internexin, alpha-synuclein, amyloid precursor protein, beta-amyloid, calbindin, choline acetyltransferase, excitatory amino acid transporter 1, GAP43, glial fibrillary acidic protein, glutamate receptor 2, myelin basic protein, nerve growth factor receptor (gp75), neuroblastoma marker, neurofilament 68 kD, neurofilament 160 kD, neurofilament 200 kD, neuron-specific enolase, nicotinic acetylcholine receptor alpha 4, nicotinic acetylcholine receptor beta 2, peripherin, protein gene product 9, S-100 protein, serotonin, SNAP-25, synapsin I, synaptophysin, tau, tryptophan hydroxylase, tyrosine hydroxylase, and ubiquitin.

[0191] Cluster differentiation markers are CD1a, CD1b, CD1c, CD1d, CD1e, CD2, CD3delta, CD3epsilon, CD3gamma, CD4, CD5, CD6, CD7, CD8alpha, CD8beta, CD9, CD10, CD11a, CD11b, CD11c, CDw12, CD13, CD14, CD15, CD15s, CD16a, CD16b, CDw17, CD18, CD19, CD20, CD21, CD22, CD23, CD24, CD25, CD26, CD27, CD28, CD29, CD30, CD31, CD32, CD33, CD34, CD35, CD36, CD37, CD38, CD39, CD40, CD41, CD42a, CD42b, CD42c, CD42d, CD43, CD44, CD44R, CD45, CD46, CD47, CD48, CD49a, CD49b, CD49c, CD49d, CD49e, CD49f, CD50, CD51, CD52, CD53, CD54, CD55, CD56, CD57, CD58, CD59, CDw60, CD61, CD62E, CD62L, CD62P, CD63, CD64, CD65, CD65s, CD66a, CD66b, CD66c, CD66d, CD66e, CD66f, CD68, CD69, CD70, CD71, CD72, CD73, CD74, CDw75, CDw76, CD77, CD79a, CD79b, CD80, CD81, CD82, CD83, CD84, CD85, CD86, CD87, CD88, CD89, CD90, CD91, CDw92, CDw93, CD94, CD95, CD96, CD97, CD98, CD99, CD100, CD101, CD102, CD103, CD104, CD105, CD106, CD107a, CD107b, CDw108, CD109, CD114, CD115, CD116, CD117, CDw119, CD120a, CD120b, CD121a, CDw121b, CD122, CD123, CD124, CDw125, CD126, CD127, CDw128a, CDw128b, CD130, CDw131, CD132, CD134, CD135, CDw136, CDw137, CD138, CD139, CD140a, CD140b, CD141, CD142, CD143, CD144, CDw145, CD146, CD147, CD148These include CDw149, CDw150, CD151, CD152, CD153, CD154, CD155, CD156, CD157, CD158a, CD158b, CD161, CD162, CD163, CD164, CD165, CD166, and TCR zeta.

[0192] Other cellular markers include centromere protein-F (CENP-F), dianthin, involucrin, lamins A&C [XB 10], LAP-70, mucins, nuclear pore complex proteins, p180 lamellar body proteins, ran, r, cathepsin D, Ps2 protein, Her2-neu, P53, S100, epithelial marker antigen (EMA), TdT, MB2, MB3, PCNA, and Ki67.

[0193] Still other suitable markers for detection include those shown in Table 2 below: [Table 2] TIFF0007720412000015.tif172164 TIFF0007720412000016.tif174164 TIFF0007720412000017.tif172164 TIFF0007720412000018.tif175164 TIFF0007720412000019.tif152164 TIFF0007720412000020.tif172164 TIFF0007720412000021.tif167164 TIFF0007720412000022.tif159164 TIFF0007720412000023.tif135164

[0194] Other downstream processing steps and system components

[0195] The system 200 of the present disclosure may be coupled to a biological specimen processing device that can perform one or more preparation processes on the tissue specimen, such as after the estimated time to fixation has been determined and / or after the biological specimen has been fixed according to the present disclosure. Preparation processes may include, but are not limited to, deparaffinizing the specimen, conditioning the specimen (e.g., cell conditioning), staining the specimen, performing antigen retrieval, immunohistochemical staining (including labeling) or other reactions, and / or performing in situ hybridization (e.g., SISH, FISH, etc.) staining (including labeling) or other reactions, as well as other processes to prepare the specimen for microscopy, microanalysis, mass spectrometry, or other analytical methods.

[0196] The processor can apply fixatives to the specimen, including crosslinkers (e.g., aldehydes such as formaldehyde, paraformaldehyde, and glutaraldehyde, as well as non-aldehyde crosslinkers), oxidizing agents (e.g., metal ions and complexes such as osmium tetroxide and chromate), protein denaturants (e.g., acetic acid, methanol, ethanol), fixatives of unknown mechanism (e.g., mercuric chloride, acetone, and picric acid), compounding reagents (e.g., Carnoy's fixative, methacarn, Bouin's solution, B5 fixative), Rossmann's solution, Gendre's solution, microwave, and other fixatives (e.g., excluding volume fixatives and vapor fixatives).

[0197] If the specimen is a paraffin-embedded sample, the specimen can be deparaffinized using an appropriate deparaffinization solution. After removing the paraffin, any number of substances can be applied to the specimen in succession. The substances can be for pretreatment (e.g., reversing protein cross-links, exposing cells to acid, etc.), denaturation, hybridization, washing (e.g., stringent washing), detection (e.g., binding of visual or marker molecules to probes), amplification (e.g., amplification of proteins, genes, etc.), counterstaining, coverslipping, etc.

[0198] The specimen processing device can apply a wide range of substances to the specimen. Substances include, but are not limited to, stains, probes, reagents, rinses, and / or conditioners. Substances can be fluids (e.g., gases, liquids, or gas / liquid mixtures). Liquids can be solvents (e.g., polar solvents, nonpolar solvents, etc.), solutions (e.g., aqueous or other types of solutions), etc. Reagents can include, but are not limited to, stains, wetting agents, antibodies (e.g., monoclonal antibodies, polyclonal antibodies, etc.), antigen retrieval solutions (e.g., aqueous or non-aqueous-based antigen retrieval solutions, antigen retrieval buffers, etc.). Probes can be isolated nucleic acids or isolated synthetic oligonucleotides attached to a detectable label or reporter molecule. Labels can include radioisotopes, enzyme substrates, cofactors, ligands, chemiluminescent or fluorescent agents, haptens, and enzymes. As used herein, the term "fluid" refers to any liquid or liquid composition, including water, solvents, buffers, solutions (e.g., polar solvents, nonpolar solvents), and / or mixtures. Fluids may be aqueous or non-aqueous. Non-limiting examples of fluids include cleaning solutions, rinsing solutions, acidic solutions, alkaline solutions, transfer solutions, and hydrocarbons (e.g., alkanes, isoalkanes, and aromatic compounds, such as xylene). In some embodiments, cleaning solutions include a surfactant to facilitate spreading of the cleaning solution over the specimen-bearing surface of the slide. In some embodiments, acidic solutions include deionized water, an acid (e.g., acetic acid), and a solvent. In some embodiments, alkaline solutions include deionized water, a base, and a solvent. In some embodiments, the transfer solution includes one or more glycol ethers, such as one or more propylene-based glycol ethers (e.g., propylene glycol ether, di(propylene glycol) ether, and tri(propylene glycol) ether), ethylene-based glycol ethers (e.g., ethylene glycol ether, di(ethylene glycol) ether, and tri(ethylene glycol) ether), and functional analogs thereof.Non-limiting examples of buffering agents include citric acid, potassium dihydrogen phosphate, boric acid, diethylbarbituric acid, piperazine-N,N'-bis(2-ethanesulfonic acid), dimethylarsinic acid, 2-(N-morpholino)ethanesulfonic acid, tris(hydroxymethyl)methylamine (TRIS), 2-(N-morpholino)ethanesulfonic acid (TAPS), N,N-bis(2-hydroxyethyl)glycine (bicine), N-tris(hydroxymethyl)methylglycine (tricine), 4-2-hydroxyethyl-1-piperazineethanesulfonic acid (HEPES), 2-{[tris(hydroxymethyl)methyl]amino}ethanesulfonic acid (TES), and combinations thereof. In some embodiments, the unmasking agent is water. In other embodiments, the buffer may be composed of tris(hydroxymethyl)methylamine (TRIS), 2-(N-morpholino)ethanesulfonic acid (TAPS), N,N-bis(2-hydroxyethyl)glycine (bicine), N-tris(hydroxymethyl)methylglycine (tricine), 4-2-hydroxyethyl-1-piperazineethanesulfonic acid (HEPES), 2-{[tris(hydroxymethyl)methyl]amino}ethanesulfonic acid (TES), or combinations thereof. Additional wash solutions, transfer solutions, acidic solutions, and alkaline solutions are described in U.S. Patent Application Publication No. 2016 / 0282374, the disclosure of which is incorporated herein by reference in its entirety.

[0199] Staining can be performed using a histochemical staining module or a separate platform (e.g., an automated IHC / ISH slide stainer). Automated IHC / ISH slide stainers typically include at least storage containers for the various reagents used in the staining protocol, a reagent dispensing unit in fluid communication with the storage containers for dispensing the reagents onto the slides, a waste removal system for removing used reagents and other waste from the slides, and a control system for coordinating the operation of the reagent dispensing unit and the waste removal system. In addition to performing the staining steps, many automated slide stainers also perform (or are compatible with other systems that perform) additional steps incidental to staining, such as slide baking (to adhere the sample to the slide), degreasing (also known as deparaffinization), antigen retrieval, counterstaining, dehydration and clearing, and coverslipping. Prichard, Overview of Automated Immunohistochemistry, Arch Pathol Lab Med., Vol. 138, pp. 1578-1582 (2014), which is incorporated herein by reference in its entirety, describes several specific examples of automated IHC / ISH slide stainers and their various features, including the intelliPATH (Biocare Medical), WAVE (Celerus Diagnostics), DAKO OMNIS and DAKO AUTOSTAINER LINK 48 (Agilent Technologies), BENCHMARK (Ventana Medical Systems, Inc.), Leica BOND, and Lab Vision Autostainer (Thermo Scientific) automated slide stainers.Ventana Medical Systems, Inc. is the assignee of several U.S. patents disclosing systems and methods for performing automated analyses, including U.S. Patent Nos. 5,650,327, 5,654,200, 6,296,809, 6,352,861, 6,827,901, and 6,943,029, as well as U.S. Patent Application Publication Nos. 20030211630 and 20040052685, each of which is incorporated herein by reference in its entirety. As used herein, the term "reagent" refers to a solution or suspension containing one or more agents capable of covalently or non-covalently reacting with, binding to, interacting with, or hybridizing to another entity. Non-limiting examples of such agents include specific binding entities, antibodies (primary antibodies, secondary antibodies, or antibody conjugates), nucleic acid probes, oligonucleotide sequences, detection probes, chemical moieties with reactive or protected functional groups, enzymes, solutions or suspensions of dye or stain molecules.

[0200] Commercially available staining units typically operate on one of the following principles: (1) open individual slide staining, in which the slide is positioned horizontally and reagents are dispensed as a puddle onto the surface of the slide containing the tissue sample (as implemented, for example, in the DAKO AUTOSTAINER Link 48 (Agilent Technologies) and intelliPATH (Biocare Medical) stainers); (2) liquid overlay technique, in which reagents are covered by or dispensed through an inert fluid layer deposited on the sample (as implemented, for example, in the VENTANA BenchMark and DISCOVERY stainers); or (3) capillary gap staining, in which the slide surface is placed near another surface (such as another slide or cover plate) to create a narrow gap through which capillary forces draw the liquid reagents in contact with the sample (as implemented, for example, in the staining principle used in the DAKO TECHMATE, Leica BOND, and DAKO OMNIS stainers). Even after repeated capillary gap staining, the fluids in the gap do not mix (e.g., in DAKO TECHMATE and Leica BOND). A variation of capillary gap staining, called dynamic gap staining, uses capillary forces to apply a sample to a slide, followed by reagent mixing by translating parallel surfaces relative to each other during incubation (e.g., the staining principle implemented in the DAKO OMNIS slide stainer (Agilent)). In translational gap staining, a translatable head is positioned above the slide. The underside of the head is spaced from the slide by a first gap small enough to allow a liquid meniscus to form from the liquid on the slide during slide translation. A mixing extension, having a lateral dimension smaller than the width of the slide, extends from the underside of the translatable head, defining a second gap between the mixing extension and the slide that is smaller than the first gap. During translation of the head, the lateral dimension of the mixing extension is sufficient to cause a lateral movement in the liquid on the slide generally in a direction from the second gap to the first gap. See WO 2011-139978.Recently, it has been proposed to use inkjet technology to deposit reagents onto slides. See WO 2016-170008. This list of staining techniques is not intended to be comprehensive, and any fully or semi-automated system for performing biomarker staining can be incorporated into the histochemical staining platform.

[0201] If morphologically stained samples are also desired, automated H&E staining platforms can be used. Automated systems for performing H&E staining typically operate on one of two staining principles: batch staining (also known as "dip 'n dunk") or individual slide staining. Batch stainers generally use a reagent vat or tank into which many slides are simultaneously immersed. Individual slide stainers, on the other hand, apply reagent directly to each slide, with no two slides sharing the same aliquot of reagent. Examples of commercially available H&E stainers include Roche's VENTANA SYMPHONY (individual slide stainer) and VENTANA HE 600 (individual slide stainer) series H&E stainers; Agilent Technologies' Dako CoverStainer (batch stainer); and Leica Biosystems Nussloch GmbH's Leica ST4020 Small Linear Stainer (batch stainer), Leica ST5020 Multistainer (batch stainer), and Leica ST5010 Autostainer XL series (batch stainers) H&E stainers.

[0202] After the specimen is stained, the stained sample can be manually analyzed under a microscope, and / or digital images of the stained sample can be acquired for archiving and / or digital analysis. Digital images can be captured via a scanning platform, such as a slide scanner, capable of scanning stained slides at 20x, 40x, or other magnifications to generate high-resolution whole-slide digital images. At a basic level, a typical slide scanner includes at least the following: (1) a microscope with a lens objective; (2) a light source (such as halogen, light-emitting diode, white light, and / or a multispectral light source, depending on the dye); (3) robotics for moving the glass slide and / or moving the optical element around the slide; (4) one or more digital cameras for image capture; and (5) a computer and associated software for controlling the robotics and manipulating, managing, and displaying the digital slides. Digital data for multiple different XY positions (and possibly multiple Z planes) on the slide are captured by the camera's charge-coupled device (CCD), and these images are combined to form a composite image of the entire scanned surface. Common methods for achieving this include:

[0203] (1) Tile-based scanning, in which a slide stage or optical system is moved in very small increments to capture square image frames that slightly overlap adjacent squares, and the captured squares are then automatically matched together to create a composite image.

[0204] (2) Line-based scanning, in which the slide stage moves in a single axis during acquisition to capture multiple composite image "strips," which can then be matched together to form a larger composite image.

[0205] A detailed overview of various scanners (both fluorescent and brightfield) can be found in Farahani et al., Whole slide imaging in pathology: advantages, limitations, and emerging perspectives, Pathology and Laboratory Medicine Int'l, Vol. 7, pp. 23-33 (June 2015), the contents of which are incorporated by reference in their entirety. Examples of commercially available slide scanners include: 3DHistech PANNORAMIC SCAN II; DigiPath PATHSCOPE; Hamamatsu NANOZOOMER RS, HT, and XR; Huron TISSUESCOPE 4000, 4000XT, and HS; Leica SCANSCOPE AT, AT2, CS, FL, and SCN400; Mikroscan D2; Olympus VS120-SL; Omnyx VL4 and VL120; PerkinElmer LAMINA; Philips ULTRA-FAST SCANNER; Sakura Finetek VISIONTEK; Unic PRECICE 500, and PRECICE 600x; VENTANA ISCAN COREO and ISCAN HT; and Zeiss AXIO SCAN.Z1. Other exemplary systems and features can be found, for example, in International Publication No. 2011-049608 or U.S. Patent Application No. 61 / 533,114, filed September 9, 2011, entitled IMAGING SYSTEMS, CASSETTES, AND METHODS OF USING THE SAME, the contents of which are incorporated by reference in their entireties.

[0206] In some embodiments, any imaging may be accomplished using any of the systems disclosed in U.S. Patent Nos. 10,317,666 and 10,313,606, the disclosures of which are incorporated herein by reference in their entireties. The imaging device may be a brightfield imager, such as the iScan Coreo™ brightfield scanner or DP 200 scanner sold by Ventana Medical Systems, Inc.

[0207] In some cases, images may be analyzed by an image analysis system. The image analysis system may include one or more computing devices, such as a desktop computer, a laptop, a tablet, a smartphone, a server, a special-purpose computing device, or any other type of electronic device, capable of performing the techniques and operations described herein. In some embodiments, the image analysis system may be implemented as a single device. In other embodiments, the image analysis system may be implemented as a combination of two or more devices that together perform various functionalities discussed herein. For example, the image analysis system may include one or more server computers and one or more client computers communicatively connected to each other via one or more local area networks and / or wide area networks (e.g., the Internet). The image analysis system typically includes at least a memory, a processor, and a display. The memory may include any combination of any type of volatile or non-volatile memory, such as random access memory (RAM), read-only memory such as electrically erasable programmable read-only memory (EEPROM), flash memory, a hard drive, a solid-state drive, an optical disk, etc. It is understood that the memory may be included in a single device or distributed across two or more devices. The processor may include one or more processors of any type, such as a central processing unit (CPU), a graphics processing unit (GPU), a dedicated signal or image processor, a field programmable gate array (FPGA), a tensor processing unit (TPU), etc. It is understood that the processors may be included in a single device or distributed across two or more devices. The display may be implemented using any suitable technology, such as LCD, LED, OLED, TFT, plasma, etc. In some implementations, the display may be a touch-sensitive display (touch screen). Image analysis systems also typically include a software system stored on a memory with a set of instructions executable on the processor, the instructions including various image analysis tasks, such as object identification, stain intensity quantification, etc.Exemplary commercially available software packages useful for implementing the modules disclosed herein include VENTANA VIRTUOSO; Definiens TISSUE STUDIO, DEVELOPER XD, and IMAGE MINER; and Visopharm BIOTOPIX, ONCOTOPIX, and STEREOTOPIX software packages.

[0208] After the specimen has been processed, the user can transfer the slide containing the specimen to an imaging device. In some embodiments, the imaging device is a brightfield imager slide scanner. One brightfield imager is the iScan Coreo brightfield scanner sold by Ventana Medical Systems, Inc. In automated embodiments, the imaging device is a digital pathology device as disclosed in International Patent Application No. PCT / US2010 / 002772 (Publication No. WO 2011 / 049608), entitled "IMAGING SYSTEM AND TECHNIQUES," or as disclosed in U.S. Patent Application No. 61 / 533,114, filed September 9, 2011, entitled "IMAGING SYSTEMS, CASSETTES, AND METHODS OF USING THE SAME." International Patent Application No. PCT / US2010 / 002772 and U.S. Patent Application No. 61 / 533,114 are incorporated by reference in their entireties.

[0209] Embodiments of the subject matter and operations described herein may be implemented in digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or one or more combinations thereof. Embodiments of the subject matter described herein may be implemented as one or more computer programs, e.g., one or more modules of computer program instructions encoded on a computer storage medium for execution by or to control the operation of a data processing device. Any of the modules described herein may include logic executed by a processor. As used herein, "logic" refers to any information in the form of instruction signals and / or data that can be applied to affect the operation of a processor. Software is an example of logic.

[0210] A computer storage medium may be or be included in a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or one or more combinations thereof. Further, a computer storage medium is not a propagating signal, but a computer storage medium may be a source or destination of computer program instructions encoded in an artificially generated propagated signal. A computer storage medium may also be or be included in one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). The operations described herein may be implemented as operations performed by a data processing device on data stored in one or more computer-readable storage devices or received from other sources.

[0211] The term "programmed processor" encompasses all types of devices, apparatus, and machines for processing data, including, for example, a programmable microprocessor, a computer, a system on a chip, or a combination of the foregoing. An apparatus may include special-purpose logic circuitry such as an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, an apparatus may also include code that creates an execution environment for the computer program, such as code comprising processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or one or more combinations thereof. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, or grid computing infrastructures.

[0212] A computer program (also referred to as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted, declarative, or procedural, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files storing one or more modules, subprograms, or portions of code). A computer program may be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0213] The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows may be performed by, and apparatus may be implemented as, special purpose logic circuitry such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0214] Processors suitable for executing a computer program include, by way of example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor for performing actions in accordance with the instructions and one or more memory devices for storing instructions and data. Typically, a computer also includes one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data, or is operatively coupled to receive data, transfer data, or both. However, a computer does not require such devices. Furthermore, a computer may be incorporated into another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Suitable devices for storing computer program instructions and data include, by way of example, all forms of non-volatile memory, media and memory devices, including semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices, magnetic disks, e.g., internal hard disks or removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0215] To provide for user interaction, embodiments of the subject matter described herein may be implemented on a computer having a display device, e.g., an LCD (liquid crystal display), LED (light emitting diode) display, or OLED (organic light emitting diode) display, for displaying information to the user, and a keyboard and pointing device, e.g., a mouse or trackball, by which the user can provide input to the computer. In some implementations, a touchscreen may be used to display information and receive input from the user. Other types of devices may also be used to provide interaction with the user. For example, feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback. Also, input from the user may be received in any format, including acoustic, speech, or tactile input. Additionally, the computer may interact with the user by sending documents to and receiving documents from devices used by the user, such as, for example, by sending web pages to a web browser on the user's client device in response to a request received from the web browser.

[0216] Embodiments of the subject matter described herein may be implemented in a computing system that includes a back-end component, such as a data server, or includes a middleware component such as an application server, or includes a front-end component, such as a client computer having a graphical user interface or web browser through which a user can interact with an implementation of the subject matter described herein, or any combination of one or more such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication, such as a communications network. Examples of communications networks include local area networks (“LANs”) and wide area networks (“WANs”), inter-networks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). For example, network 20 of FIG. 2 may include one or more local area networks.

[0217] A computing system may include any number of clients and servers. Clients and servers are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server sends data (e.g., HTML pages) to a client device (e.g., for the purpose of displaying the data and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., the result of a user's operation) may be received from the client device at the server.

[0218] Other methods for calculating the TOF of samples immersed in one or more fixatives

[0219] In some embodiments, the diffusion rate can be monitored by a system of acoustic probes based on the different acoustic properties of the formalin-soaked tissue sample. Such systems for diffusion monitoring and experimental TOF measurements are described in further detail in U.S. Patent Application Publication Nos. 2013 / 0224791, 2017 / 0284969, 2017 / 0336363, 2017 / 0284920, and 2017 / 0284859, the disclosures of which are each incorporated herein by reference in their entirety.

[0220] Further examples of suitable systems and methods for TOF monitoring are described in International Publication No. 2016 / 097163 and U.S. Patent Application Publication No. 2017 / 0284859, the contents of which are also incorporated by reference herein to the extent they do not contradict this disclosure. The referenced applications describe solid tissue samples that are analyzed based on acoustic properties that are continuously or periodically monitored to assess the state and condition of the tissue sample during processing, contacting it with a liquid fixative that migrates through the tissue sample and diffuses substantially throughout the entire thickness of the tissue sample. For example, fixatives such as formalin, which have a bulk modulus greater than that of interstitial fluid, can significantly alter TOF when displacing the interstitial fluid. The acoustic properties of the tissue sample can change as liquid reagents (e.g., liquid fixatives) migrate through the sample. The acoustic properties of the sample can change, for example, during presoaking (e.g., diffusion of a cold fixative), fixation, staining, etc. During fixation (e.g., crosslinking), the transmission rate of acoustic energy can change as the tissue sample becomes more strongly crosslinked. Real-time monitoring can be used to accurately track the movement of the fixative through the sample. [Example]

[0221] Example

[0222] Example 1 - Exemplary time courses of past, present, and future reliability models

[0223] An exemplary time course showing multiple time points of TOF data collection is shown in Figure 5. In the series of plots, initially, the predictive power of the TOF curve is very poor, as indicated by the large dotted line that deviates significantly from the black line of best fit (see top plot). All three parameters fluctuate considerably as more TOF data is collected (a-d). After approximately 2.83 hours of active diffusion, the model determines that the tissue TOF curve is considered valid and predicts that the tissue sample will be adequately stained after 4.41 hours of diffusion. The model continues to collect more data, shifting the estimate slightly longer to 4.7 hours, at which point the sample is considered sufficiently fixed.

[0224] A more detailed version of each of the past, present, and future reliability models is presented in Figure 6, where the criteria for all three reliability models are plotted against time. From the graph, it can be observed that initially the future and present reliability models are met, but as more data is collected, the criteria reverse. Therefore, the fact that the past criteria were not met at the start of the experiment was crucial to preventing the TOF signal from being considered valid when it was in fact not.

[0225] Figure 7 presents another example of criteria selection for a colon tissue sample. In this particular example, the future and past confidence models were met earlier, and the current confidence model has not yet been met, so the tissue is not considered valid.

[0226] Figure 8 shows a further example of criteria selection for a kidney tissue sample. In this case, the future confidence model is the last confidence model to be met, but the current confidence model is met almost immediately. The past confidence model is met initially, but then the reverse occurs. Thus, the failure of the future confidence model prevented the tissue from being called valid at approximately 1.5 hours.

[0227] Figure 9 shows yet another example of criteria selection for a breast tissue sample. In this case, the past and present confidence models "bounce" back and forth, but the future confidence model is not filled until 2 hours later, preventing a signal from being called valid before it actually is.

[0228] The above example illustrates how the three confidence models work together to create a system and / or method that is not determined to be valid until the TOF signal is actually valid. TOF has been previously disclosed and demonstrated in (see Figure 10A) for many different types of tissue. Here, 105 samples from numerous tissue types were used to evaluate the accuracy of the confidence models, and therefore the systems and methods described herein. The cumulative results are shown in Figure 10B, where the time required to receive a valid fit is plotted against the calculated optimal fixation time. On average, the sample fit converged to a valid fit before the sample was optimally fit for 102 of the 105 samples (97%). On average, the tissue was determined to have a valid fit in 2.04 hours, requiring an average fixation time of 2.96 hours. This is an important finding because it indicates that, on average, it takes 0.92 hours for the tissue to be in place after the TOF signal is proven valid.

[0229] Another way to evaluate the accuracy of the system and method using the three reliability models described herein is to retrospectively compare the predicted optimal fixation times at convergence and at the end of the experiment. These results are plotted in Figure 11A. On average, the optimal fixation times at convergence and at the end of the experiment differed by only 0.45 h, indicating high accuracy, as there was no significant difference between the two values. Furthermore, at convergence, the TOF curve had a 95% confidence interval of only 0.44 ns, and the decay constant had a 95% confidence interval of only 7 min (see Figure 11B). Both of these figures were very low, consistent with a high-fidelity fit at which the TOG signal was considered valid.

[0230] Example 2

[0231] summary

[0232] Modern histopathology is based on the fundamental principle of tissue fixation, traditionally using room-temperature aqueous formaldehyde (formalin) to preserve tissues in a viable state. Despite its importance, there are currently no analytical methods to detect fixation, and as a result, in clinical practice, fixation is highly variable and a source of significant error. It has previously been shown that immersion in cold formalin followed by heated formalin is beneficial for preserving tissue morphology because the cold phase allows for complete, unimpeded diffusion of formaldehyde before crosslinking is rapidly initiated during the heated phase. Furthermore, a novel dual-temperature fixation has been combined with an ultrasensitive acoustic monitoring technique that can actively detect formalin diffusing into tissue. Here, a predictive statistical model is developed to determine when tissue has been adequately diffused based on real-time time-of-flight (TOF) signals. The model was trained based on the morphology and characteristic diffusion curves of 30 tonsillar cores. In the test model, a set of 87 different tonsillar samples was fixed using four different protocols: dynamic fixation (C / H:dynamic, N = 18) according to the disclosed prediction algorithm; the gold standard 24-hour room temperature fixation (RT:24h, N = 24); 6 hours in cold formalin followed by 1 hour in heated formalin (C / H:6+1, N = 21); and 2 hours in cold formalin followed by 1 hour in heated formalin (N = 24). For samples fixed using the dynamic fixation protocol, digital pathology analysis revealed spatially uniform FOXP3 staining with statistically equivalent staining coverage levels to the RT:24h and C / H:6+1 fixation protocols. For comparison, intentionally fixed C / H:2+1 samples significantly suppressed FOXP3 staining (p < 0.002). Furthermore, the disclosed dynamic fixation protocol resulted in bcl-2 staining consistent with standard fixation techniques. Dynamically fixed specimens required only 4.2 hours of immersion in cold formalin, on average, demonstrating a significant workflow improvement. These results establish that the disclosed and developed system can accurately predict when a specimen is sufficiently fixed and will produce high-quality histochemical staining.Indeed, the disclosed system and method have been successfully demonstrated to enable assessment of fixation quality in real time. Ultimately, it is believed that future histology laboratories will be able to utilize this analytical method to standardize and optimize tissue fixation as part of a rapid and well-documented pre-analytical workflow.

[0233] Introduction

[0234] Clinical tissue processing techniques "fix" tissues using crosslinking agents that block intracellular metabolism and preserve clear, transparent cellular morphology. The most common fixative is 10% neutral buffered formalin (NBF), an aqueous solution of formaldehyde in a buffer that has been used for a century (Fox et al., 1985). Currently, appropriate fixation protocols are determined empirically by examining histologically stained tissues for appropriate morphological features. The results are a mixed bag of adequate and inadequate morphology, depending on the operator, facility, tissue type, and biomarkers in question. Furthermore, by the time morphology is examined, it is too late to improve tissue quality, so initial proper fixation is crucial.

[0235] Tissue fixation is a time-consuming process that typically takes hours to days, depending on the type and size of the tissue. With increasing pressure to reduce the turnaround time for patient care, rapid fixation protocols have been introduced. One such technique that has already been used is to increase the temperature of the fixative to increase the crosslinking rate (Ferris et al., 2009; Iesurum et al., 2006; Antunes et al., 2006; Looi and Loh, 2005; Hafajee and Leong, 2004; Adams, 2004; Morales et al., 2002; Arber, 2002; Ruijter et al., 1997; Boon, 1996; Ainley and Ironside, 1994; Boon and Marani, 1991; Kok and Boon, 1990; Leong, 1988; Boon et al., 1988; Leong and Duncis, 1986; Kok et al., 1986; Boon, Kok, and Ouwerkerk-Noordam, 1986; Leong, Daymon, and Milios, 1985). While practically effective, the use of elevated fixation temperatures has led to numerous reports of poor tissue morphology based on hematoxylin and eosin (H&E) staining and variability in other molecular analyses, such as routine immunohistochemistry (IHC) staining (Durgun-Yucel et al., 1992; Dawson, 1972; Ericsson and Biberfeld, 1967). Biologically, the use of heat fixatives serves to crosslink proteins on the outside of the tissue sample while compromising the structure of proteins in the center where sufficient fixative has not penetrated. A more promising rapid method that results in excellent tissue fixation uses 10% NBF in two temperature zones (low + high) (Chafin et al., 2013; Theiss et al., 2014). The low-temperature step allows for adequate diffusion of formaldehyde into the tissue interior, followed by a short heating phase that rapidly forms formaldehyde-based crosslinks.

[0236] Currently, there is no established method to actively monitor either the diffusion of formaldehyde into tissue or the actual formation of crosslinks. Insufficiently diffused tissue only crosslinks and fixes where the fixative penetrates, forming an outer ring of adequately fixed tissue. Inadequate fixation is a major source of reported error in anatomic pathology laboratories (Mathews, Newbury, and Housser, 2011; Engel and Moore, 2011; De Marzo et al., 2002; Plebani et al., 2015; Plebani, 2015; Bonini et al., 2002). Current tissue fixation protocols lack real-time monitoring, so there is no way to ensure sufficient formaldehyde concentrations in tissue or, conversely, to know if a sample has been overfixed, which also has a detrimental effect on staining quality (Singhal et al., 2016; Arber, 2002). In short, current fixation techniques do not provide quality assurance or tracking capabilities for tissue processing laboratories. This means that if a sample is improperly fixed, expensive reprocessing will be necessary if another sample can be obtained. Several methods exist for statically detecting diffusion, including optical, ultrasound, and MRI, but these detection mechanisms are not implemented due to tissue processing to better preserve cancer indicators (Partridge et al., 2012; Uhl M., 2014; Tanimoto et al., 2007; Petrasek and Schwille, 2008; DuMond and Youtz, 2004). Some researchers have immersed tissue in radioactive formaldehyde and measured fluid penetration after exposure to photographic film as a measure of diffusion rate (Helander, 1994). However, little radioactivity is actually incorporated into the tissue, and long exposure times have led to ambiguous and unreliable results. Others have used ultrasound monitoring to examine cross-linking by comparing unfixed samples to fixed tissue ( Oldenburg and Boppart, 2010 ; Hall et al., 2000 ; Bamber, Hill, and King, 1981 ; Bamber and Hill, 1981 ; Bamber et al., 1979 ; Bamber and Hill, 1979 ).Finally, none of these techniques allow for real-time monitoring of when changes can be implemented to ensure good tissue fixation and proper functional staining.

[0237] Therefore, a dynamic method for optimizing tissue fixation using real-time detection of formalin diffusion was developed (as described herein) to ensure sufficient formaldehyde is present throughout the tissue to ensure proper staining. An automated system capable of detecting fixative penetration into live biological tissue using acoustic time-of-flight (TOF) technology, which exploits the discrete sound speeds of interstitial fluid and formalin, has previously been described (Bauer et al., 2016). As formalin diffuses into a tissue specimen and replaces replaceable fluids (e.g., interstitial fluid), the overall composition of the tissue physically changes, resulting in TOF differences. As faster formalin diffuses into the sample, the net sound speed of the tissue increases, resulting in a monotonically decreasing TOF signal. This disclosure relates to a real-time statistical model and custom-modified tissue processor system that determines when a sample is sufficiently adequately diffused to produce high-quality staining from downstream IHC assays.

[0238] method

[0239] Tissue acquisition and fixation

[0240] Human tonsil tissue was obtained fresh and unfixed from a local Tucson, Arizona, hospital under contractual agreement with an approved protocol. Whole tonsils from same-day surgery were transported to Roche Tissue Diagnostics on wet ice in biohazard bags. A 6 mm diameter biopsy punch (Miltex #33-36) was used to obtain a precisely sized tonsil tissue sample. For cold and hot fixation, a 6 mm tonsil core was placed in 10% NBF (saturated aqueous formaldehyde buffered to pH 6.8-7.2 with 100 mM phosphate buffer, Fisher Scientific, Houston, TX) pre-cooled to 4°C. The sample was then removed and placed in NBF at 45°C for an additional hour to initiate crosslinking. Another punch was placed in room temperature NBF for 24 hours to serve as a positive control for adequately fixed tissue. An additional biopsy punch was fixed in a tissue cassette and placed between the TOF sensors of the automated fixator. After fixation, samples were processed in a commercial tissue processor set on an overnight cycle and embedded in wax.

[0241] Time-of-flight measurements

[0242] Briefly, a pair of 4 MHz focused transducers was spatially aligned, and the tissue sample was placed at their common focus. One transducer was programmed to emit a sinusoidal pulse that was detected by the companion transducer after traversing formalin and tissue; the received pulse was used to calculate transit time. An initial calibration TOF reading was obtained by measuring the signal through formalin alone. This baseline reading was subtracted from the TOF in the presence of tissue to isolate phase shifts from the tissue. This detection method simultaneously isolated acoustic TOF shifts due to diffusion and compensated for environmentally induced formalin variations. Multiple TOF measurements were recorded across each tissue specimen, and the spatially averaged signal was recorded as a representative of the overall formalin diffusion rate in the tissue. In practice, the morphology of the TOF diffusion signals from multiple tissue types correlated well with a single exponential decay function (Lerch et al., 2017).

[0243] histology

[0244] Paraffin tissue blocks were sectioned at 4 μm thickness and mounted on Fisherbrand™ Superfrost™ Plus microscope slides (Thermo Fisher Scientific). To assess tissue morphology, one section was stained with H&E using a Ventana Medical Systems HE600 automated staining system. To further assess IHC staining intensity and coverage, serial sections from each block were stained with diaminobenzidine (DAB) stain for anti-FOXP3 (SP33) or anti-bcl-2 (SP66) using a Ventana Benchmark Ultra XT automated staining system according to the manufacturer's protocol.

[0245] Statistical Modeling

[0246] The statistical model used to calculate in real time when the TOF signal represents the actual diffusion rate in tissue was first developed using custom-developed code written in MATLAB (Mathworks) using multiple functions from the Statistics and Machine Learning toolbox. When the final model was translated into the laboratory for implementation on our TOF scanning hardware, it was converted to Python using several libraries, including numpy, matplotlib, scipy, and kapteyn.

[0247] Imaging and Image Processing

[0248] Each slide was imaged at 20x magnification using a whole-slide scanner (VENTANA iScan HT Slide Scanner) with DAB stain and hematoxylin counterstain. Images were analyzed using a custom-developed software package written in MATLAB. A segmentation algorithm distinguished the entire tissue section from the background. A separate algorithm identified regions within the tissue footprint containing unstained tissue (e.g., pores, fissures, and stroma) to obtain the most representative statistics around the percentage of tissue staining. Transmission images were log-transformed and spectrally unmixed to separate DAB staining from hematoxylin counterstaining. Unmixed DAB density mapping was used to determine which pixels were DAB-positive using a global threshold. Raw DAB concentrations were also recorded so that the intensity of DAB staining could be analyzed. To test the effect of improper fixation, an algorithm was written to calculate the Euclidean distance to the nearest edge pixel so that DAB staining intensity and DAB positivity could be tested for different fixation protocols.

[0249] result

[0250] Criteria for real-time prediction of dyeing quality

[0251] To build a model capable of predicting optimal fixation quality in real time, it was necessary to understand the relationship between formalin diffusion and morphological characteristics. Diffusion is primarily controlled by concentration gradients and time according to Fick's law of diffusion. Several time-course experiments were performed using 6 mm cores of human tonsil tissue immersed in NBF at 4°C, followed by 1 hour in NBF at 45°C (Lerch et al., 2017). After analyzing multiple experiments, a minimum of 3 hours of cold NBF (C / H:3+1) was determined to produce acceptable tissue morphology. Tissue morphology improved with 5 hours in cold NBF (C / H:5+1), but further cold immersion time did not provide additional benefit. Multiple cores were then examined, confirming that the C / H:5+1 protocol produced high-quality staining (see cumulative results in Figure 12A).

[0252] In the previous section, we empirically determined the diffusion time required to produce high-quality H&E staining. Next, we developed analytical metrics to quantitatively characterize the diffusion properties of human tonsillar tissue and determine when a sample has adequately diffused. Several whole tonsillar tissues were cored to a diameter of 6 mm. A total of 38 6 mm tonsillar samples were measured in cold (7 ± 0.5 °C) 10% NBF. Of the 38 samples, 14 were monitored for 3 hours, and the remaining 24 samples were monitored for 5 hours. For each sample, diffusion was measured across the entire sample (at 1 mm intervals), and a spatially averaged TOF curve was calculated. The characteristic TOF-based diffusion signal was highly correlated with a single exponential curve of the following form: JPEG0007720412000024.jpg1574

[0253] where C is a constant offset in nanoseconds and A avg where τ is the amplitude of the exponential decay in nanoseconds, and τ is the average decay constant of the tissue in hours. Samples scanned for 3 and 5 hours had average decay constants of 2.33 and 2.72 hours, respectively. The difference of 0.39 hours was not statistically significant, indicating that the two data sets faithfully measured the same physical phenomenon. This established that the TOF measurement system produced consistent and reproducible results for long and short diffusion times.

[0254] After validating the diffusion monitoring system, a dataset of thirty-eight (38) 6 mm tonsil samples was analyzed to find correlations between the diffusion properties of each tissue and the empirically determined diffusion times required to produce ideal downstream staining. Numerous analytical techniques were used, including multivariate analysis, cluster-based algorithms, signal derivative characterization, and principal component analysis. Gradient-based analysis, which physically represents the diffusion rate, was found to provide ideal and significant discrimination between samples aged 3 hours (i.e., adequately stained) versus 5 hours (i.e., optimally stained throughout the sample) in cold formalin. To significantly mitigate noise and more accurately represent the active rate of diffusion, the derivative of the TOF signal was calculated based on a fit to a single exponential function. Furthermore, ideal discrimination between the two datasets was achieved by amplitude normalizing each signal. JPEG0007720412000025.jpg1781

[0255] where m is the derivative of the amplitude-normalized TOF signal at time t0, and the brackets indicate the units of the slope of the percent TOF change per hour of diffusion. Figure 13A shows the normalized slopes for each sample at 3 and 5 hours, respectively. While the average diffusion rate at 3 hours was -11.3% / hour, at 5 hours the diffusion rate significantly slowed to -5.3% / hour, with some samples approaching complete osmotic equilibrium as indicated by near-zero diffusion rates. The different distributions of normalized diffusion rates at 3 and 5 hours were highly statistically significant (p<2e-15), indicating a dramatic and physically realistic difference in diffusion rates at 3 hours versus 5 hours. Thus, TOF-based diffusion metrics and H&E-based staining quality were highly correlated, demonstrating that, when properly calibrated, our diffusion monitoring system could fundamentally predict final staining quality. Given this validation, the following equation was solved for the time required to reach a baseline rate of NBF diffusion: JPEG0007720412000026.jpg1386

[0256] In the formula, t done is the threshold gradient (m thresh), where the |...| symbol indicates an absolute value. For a given tissue-specific decay constant and normalized diffusion rate, this equation can be used to calculate the time a sample needs to be in cold formalin to reach the threshold diffusion rate. To assess diffusion rate as a predictor of staining quality, three threshold slope values (m thres = -7.4% / hr, -8.0% / hr, -10.4% / hr) were selected for evaluation. Note that larger absolute slope values represent more fluid exchange per hour. Thus, as the active diffusion of the sample slows, the diffusion rate approaches osmotic equilibrium (i.e., 0% / hr). Therefore, a larger threshold slope criterion predicts that the sample will have adequate formaldehyde sooner. This is shown graphically in Figure 13B for a representative 6 mm section of human tonsil, where a decreased diffusion rate leads to a longer completion time.

[0257] Additionally, the predicted completion times of the tissues in each data set (3 h and 5 h) are displayed in Figures 13C-13E, as calculated from the above equation. For example, Figure 13C displays the predicted completion times for a maximum threshold gradient of -10.4% / h. This gradient criterion predicted a mean completion time of 3.27 h. However, six (6) samples (16%) had predicted completion times less than 3 h, and from our previous tissue staining results, these samples were known to not diffuse sufficiently throughout. Furthermore, one sample was incorrectly identified with this gradient criterion. Based on these findings, all samples should not have enough formalin to stain acceptably if their diffusion rate is -10.4% / h. An intermediate threshold gradient value of -8.0% / h would result in ideal discrimination between the two data sets and reasonable completion times of 3-5 h. However, to be as conservative as possible, a minimum threshold gradient value of -7.4% / h was selected as the criterion for when the sample is optimally stained throughout. By this metric, a 6 mm tonsil core was predicted to take 3.21 to 4.96 hours from downstream histological staining, which was understood to be reasonable. Based on these experiments and analyses, the slower the real-time rate of normalized diffusion of the sample, the longer it would take to complete the process. TIFF0007720412000027.tif845

[0258] The specimens have sufficient formalin throughout to ensure optimal and uniform histological staining.

[0259] Statistical models for real-time verification of TOF signals

[0260] Practical implementation of TOF technology requires that diffusion curves be analyzed with temporally sparse data in the absence of a ground-truth assessment of the tissue's true temporal diffusion profile. For example, prediction of when a sample is optimally fixed is based on the detected rate of NBF diffusion at a given time. However, this prediction is only valid if the exponential fit of the tissue's diffusion curve represents the tissue's actual diffusion rate. Particularly at the beginning of an experiment when data are sparse, the detected diffusion rate can vary significantly due to various sources (e.g., tissue deformation, thermal noise, fluid variables, etc.) in addition to the noise inherent in TOF calculations. To overcome these limitations, a statistical model was developed to verify when the TOF curve converged to the tissue's true diffusion profile and, therefore, was accurate enough to make predictions regarding whether the sample was adequately fixed.

[0261] The solution developed was a statistical model with three independent components that queried different components of the TOF signal. All three components were continuously recalculated as TOF data points were calculated during cryo-NBF diffusion. The diffusion profile was judged to be accurate if all three statistical parameters were simultaneously satisfied.

[0262] Condition #1 (Past Confidence Model): This condition establishes that the current TOF fit is consistent with previously collected data. In this example, the decay constants of a single exponential line must converge as defined by 10 consecutive decay constants that differ by less than 2% relative to the average of the previous six decay constants.

[0263] Condition #2 (Current Confidence Model): This condition establishes the statistical confidence in the currently collected data. In this example, the 95% confidence interval for the most recent decay constant must be less than 0.7 hours.

[0264] Condition #3 (Future Confidence Model): This condition establishes that there is sufficient statistical confidence to predict future TOF values. In this example, the 95% confidence interval of the TOF signal over all time (past, present, and future) must be less than 2 ns.

[0265] When all three statistical conditions were met, the confidence model verified that the TOF signal from the tissue represented the actual diffusion rate and predicted the time when the tissue would be properly immobilized according to Equation 14 with the threshold condition set forth in Equation 15. A graphical representation of the real-time statistical model is shown in Figure 14A, where the TOF diffusion signal was determined to be representative at 3.08 hours, at which point the predictive confidence model calculated that the tissue would be properly diffused at 4.47 hours. The current, future, and past confidence model calculations, up to the point at which they were met, are plotted in Figures 14B, 14C, and 14D, respectively.

[0266] The overall validity of the methodology was tested on 105 previously collected TOF curves (Lerch et al., 2017). This preliminary data was analyzed by a statistical model to describe how it would behave on actual empirical data. Once the model validated the signal, the predicted time of optimal fixation was calculated to be within 27 minutes of the true time, as calculated by the fit at the end of the experiment. The statistical confidence in the decay constant at convergence was ±7 minutes. These results confirm that the model, determined in real time when the TOF signal represents the actual diffusion rate in tissue, and its data-based predictions were accurate. Figure 14E plots the time to validate the characteristic diffusion curve versus predicted fixation time. On average, the model converged to a valid fit 55 minutes before the sample was adequately diffused. These results detail how the model was able to determine when biological analytes were optimally diffused almost an hour in advance, thus establishing the feasibility of a commercial implementation of TOF-based diffusion monitoring combined with real-time assessment of fixation quality.

[0267] Validation of fixation prediction model by IHC staining

[0268] To confirm that the developed predictive model truly predicted the final staining quality, we developed a digital pathology tool to objectively and repeatedly assess the staining level of each slide. Digital analysis was performed on IHC slides using DAB and a hematoxylin counterstain, which stains a single marker. A whole-slide scan was acquired for each slide. The software segmented the tissue and identified and removed unstained areas, such as connective tissue and stroma. For all areas with active staining, the software quantified the percentage of tissue that was DAB-positive. Furthermore, it calculated edge distance, defined as the shortest possible distance to the border of the tissue sample. Next, radially concentric 0.33 mm "zones" of the tissue were calculated, allowing staining to be analyzed in areas containing approximately equal concentrations of formalin. This geometric representation of the tissue allowed us to explicitly analyze the effects of improper fixation, as formalin diffuses from the periphery into the tissue, gradually reducing staining in the tissue core. A histogram was calculated, plotting the percentage of tissue staining against the distance to the nearest tissue edge. Finally, each radial zone of tissue was analyzed for appropriate staining by defining suppressed staining as DAB positivity less than half of the edge / maximum positivity. A graphic depiction of the image analysis workflow is presented in Figure 15.

[0269] To confirm that the disclosed system and method can accurately determine in real time when tissue is optimally spread, a large-scale study was conducted using 87 individual tonsil cores differentially fixed with one of four fixation protocols. For all fixation protocols, tissues were processed in a standardized manner after fixation, embedded in paraffin blocks, and slides were sectioned and stained with DAB for FOXP3 and bcl-2. The first three fixation protocols were cold NBF for 2 hours followed by heated NBF for 1 hour (C / H:2+1, N=24), cold NBF for 6 hours followed by heated NBF for 1 hour (C / H:6+1, N=21), and room temperature NBF fixation for 24 hours (RT:24 hours, N=24). Finally, we tested our real-time fixation prediction algorithm using a low-temperature + high-temperature fixation method in which tonsils were placed in chilled NBF until our system determined they were sufficiently diffused, at which point they were transferred to heated formalin for 1 hour to initiate crosslinking (C / H:Dynamic, N=18). In this experiment, the C / H:6+1 and RT:24 hour protocols represented internal controls because these fixation methods were known to produce high-quality staining. Alternatively, C / H:2+1 samples were intentionally represented under-fixed tissue. Representative images of the staining patterns for each fixation method are displayed in Figure 16. While the two standard fixation protocols produced tissue that was nearly uniformly stained, C / H:2+1 tissue significantly suppressed staining in the center of the tissue. Importantly, tissue from the C / H:Dynamic protocol produced uniform FOXP3 staining consistent with the two gold-standard fixation protocols.

[0270] Furthermore, cumulative box and whisker plots for FOXP3 staining across the 87 tissue studies are presented in Figure 17. Stain penetration depth, defined by the distance at which the stain drops to half of its edge value, is plotted in Figure 17A, and the area of adequately stained tissue is plotted in Figure 17B. For both metrics, C / H:2+1 samples showed significantly reduced staining compared to the other three fixation methods (p<0.002). Importantly, C / H:dynamic samples showed staining results consistent with both RT:24 h and C / H:6+1 samples. Furthermore, dynamically fixed samples were in cryo-NBF for only 4.2 h, meaning that the TOF prediction algorithm generated stained tissue as well as conventionally fixed tissue, but several hours faster. Furthermore, the average spatial staining profile for FOXP3 for all four fixation protocols is plotted in Figure 17C. Dynamically fixed samples again showed staining patterns consistent with C / H:6+1 and RT:24 h fixation. This is in stark contrast to the C / H:2+1 sample, which had approximately 70% less staining in the center versus the edge of the tissue.

[0271] Consideration

[0272] A major source of error in histology laboratories is caused by improperly fixed tissue, resulting in reduced staining intensity and poor morphology (Mathews, Newbury, and Housser, 2011; Engel and Moore, 2011; De Marzo et al., 2002). This study demonstrates the development of the first tissue fixation system of its kind that can determine in real time when a biological specimen has adequate formaldehyde to ensure high-quality fixation, as demonstrated by ideal, spatially uniform functional staining from downstream IHC assays. A predictive algorithm was trained using a combination of H&E-based morphology and characteristic diffusion curves from tonsillar samples. Finally, our system was validated by comparing staining results from different fixation protocols. Dynamically fixed samples with actively controlled diffusion times were found to have equivalent FOXP3 and bcl-2 staining compared to the current clinical gold standard of 24 hours of room-temperature fixation.

[0273] In today's histology laboratories, the process of tissue fixation is largely influenced by workflow considerations rather than scientific principles, and protocols can vary significantly across institutions. One difficulty with having multiple, non-standardized procedures is the inhibition of easily sharing data and results across multiple sites. Clinicians would greatly benefit from data collected from standardized specimens treated with the same fixation protocol. For example, digital pathology algorithms can output inconsistent results when analyzing specimens with improper pre-analytical processing. More consistent staining levels have been reported in properly fixed tissues compared with inadequately fixed specimens for FOXP3 and bcl-2 staining (Figure 19). Another benefit of standardizing tissue fixation is increased efficiency, reducing the amount of costly rework due to poor quality or tissue loss. This rework cost, which often incurs costs for laboratories, can be significantly reduced by a standardized system as part of a fully documented pre-analytical workflow.

[0274] A novel fixation protocol based on two temperature zones has previously been demonstrated to better preserve labile biomarkers such as phosphoproteins and mRNA (Theiss, 2017, "Increased Detection of RNA Species in Histological Tissues Using a Two-Temperature Fixation Protocol"). As the number of specimens continues to increase and diagnostic assays rely on next-generation biomarkers, faster, more efficient techniques such as two-temperature fixation will be necessary. This disclosure provides a system that utilizes this more efficient method of fixing tissue by standardizing formaldehyde infiltration. Histological practices have been in place for decades, and these practices are difficult to change. The methods and instruments described herein utilize the same reagents (10% NBF) and procedures (formaldehyde crosslinking) and therefore do not alter downstream assay protocols or results. Rather, this and other more efficient methods of tissue standardization are necessary to enable accurate preservation and quantification of analytes in the future.

[0275] While this study tested and validated the system and method using FOXP3 expression in human tonsillar samples, it is believed that the system and method are applicable to all biomarkers and tissue types. For example, additional data regarding the faithful preservation of bcl-2 by our novel fixation method is presented herein. The dynamic fixation protocol demonstrated staining comparable to that achieved with 24-hour room-temperature fixation in clinical settings (compare Figure 18 and Figure 19). The bcl-2 expression pattern did not show significant dependence on fixation quality, which was expected because bcl-2 is known as a robust clinical biomarker. This result highlights that improved fixation is particularly beneficial for labile biomarkers. An analysis of staining intensity for differentially fixed tissues is presented in Figure 20. Furthermore, diffusion data for 34 different tissue types has previously been generated, each shown to have its own characteristic diffusion rate; therefore, the disclosed predictive statistical model can be applied to multiple tissue types (Figure 20) (Bauer et al., 2016).

[0276] One of the next steps for this technology is to empirically explore the use of this prediction algorithm for other tissue types and biomarkers, particularly those known to be preanalytically sensitive. Future research will involve extending our current criteria, developed using tonsil tissue, to multiple tissue types so that universal fixation criteria can be achieved. It is hypothesized that current instrumentation can be adjusted to create a generalizable model that can optimally predict when all types of tissue are properly fixed. It is conceivable that future histology laboratories will be able to use this analytical method as part of a random-access automated processing unit that can ensure and document that each individual tissue sample is optimally fixed, truly translating tissue fixation into science.

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[0326] Additional Embodiments

[0327] Hine (Stain Technol. 1981 March;56(2):119-23) discloses a method for staining whole tissue blocks by immersing the tissue sample in a hematoxylin and eosin solution after fixation and before embedding and sectioning. Furthermore, fixation is often performed by immersing the unfixed tissue sample in a volume of fixative solution, which is allowed to diffuse throughout the tissue sample. As demonstrated by Chafin et al. (PLoS ONE 8(1):e54138.doi:10.1371 / journal.pone.0054138(2013)), failure to ensure that the fixative has sufficiently diffused throughout the tissue can compromise the integrity of the tissue sample. Thus, in one embodiment, the present system and method are applied to determine sufficient diffusion time of the fixative into the tissue sample prior to downstream processing, such as staining with a counterstain (such as hematoxylin and eosin) and / or labeling of one or more biomarkers.

[0328] In some embodiments, the present systems and methods are used to perform dual-temperature immersion fixation on tissue samples. As used herein, "dual-temperature fixation" refers to a fixation method in which tissue is first immersed in a low-temperature fixative solution for a first period of time, followed by heating the tissue for a second period of time. The low-temperature step allows the fixative solution to diffuse throughout the tissue without causing substantial cross-linking. Then, once the tissue has sufficiently diffused throughout the tissue, a heating step results in cross-linking by the fixative. The combination of low-temperature diffusion followed by a heating step results in a more completely fixed tissue sample than using standard methods. Thus, in some embodiments, the tissue sample is fixed by: (1) immersing the unfixed tissue sample in a low-temperature fixative solution and monitoring the TOF in the tissue sample using the systems and methods disclosed herein to monitor the diffusion of the fixative into the tissue sample (diffusion step); and (2) allowing the temperature of the tissue sample to be increased after a threshold TOF is measured (fixation step).

[0329] All U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications, and non-patent publications referred to herein and / or listed in the Application Data Sheet are incorporated herein by reference in their entirety. Aspects of the embodiments can be modified, if necessary, to use concepts from the various patents, applications, and publications to provide further embodiments.

[0330] While the present disclosure has been described with reference to certain exemplary embodiments, it should be understood that numerous other modifications and embodiments may be devised by those skilled in the art that would fall within the spirit and scope of the principles of the present disclosure. More particularly, reasonable variations and modifications are possible in the components and / or arrangements of the combined configuration of the subject matter within the scope of the foregoing disclosure, the drawings, and the appended claims without departing from the spirit of the present disclosure. In addition to variations and modifications of the components and / or arrangements, alternative uses will also be apparent to those skilled in the art.

Claims

1. 1. A method for estimating optimal diffusion time of a fluid into a biological specimen immersed in said fluid, comprising: (a) acquiring acoustic data at one or more locations along the biological specimen immersed in the fluid; (b) deriving time-of-flight (TOF) data from the acquired acoustic data, the derived TOF data including one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated attenuation constants; (c) simultaneously calculating at least two reliability models based on the derived TOF data, the at least two reliability models including a historical reliability model, a current reliability model, and / or a future reliability model; (d) determining when the at least two calculated reliability models each independently meet a predetermined threshold criterion; (e) estimating the time for optimal diffusion of the fluid into the biological specimen based on the TOF data corresponding to the determined time points at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion; Including, the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test; The method, wherein performing the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

2. The method of claim 1 , wherein the fluid comprises one or more fixatives.

3. The method of claim 2 , wherein the one or more fixatives are aldehyde-based fixatives.

4. The method of claim 2 , wherein the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

5. The method of claim 1 , wherein the calculated at least two confidence models include the historical confidence model and the current confidence model.

6. The method of claim 1 , wherein the calculated at least two confidence models include the past confidence model, the current confidence model, and the future confidence model, respectively.

7. 7. The method of claim 1, wherein the average attenuation constant is derived by (i) taking a calculated attenuation constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the calculated attenuation constants taken.

8. The method of claim 7 , wherein the predetermined number of TOF data points preceding the candidate TOF data point is at least about three.

9. The method of claim 1 , wherein the predetermined threshold percentage value is less than about 5%.

10. 7. The method of claim 1, wherein the predetermined threshold percentage value is less than about 2.5%.

11. The method of claim 1 , wherein the calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold.

12. 12. The method of claim 11, wherein the current confidence interval is calculated by: (a) performing a nonlinear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve; and (b) determining the confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting.

13. The method of claim 1 , wherein the calculated future confidence model satisfies a predetermined future confidence model threshold criterion when the calculated future confidence interval is below a predetermined future confidence model threshold.

14. 14. The method of claim 13, wherein the future confidence interval is calculated by: (a) performing a nonlinear regression fit of the TOF curve using all of the calculated TOF data points of the calculated TOF curve; (b) determining the confidence interval of the retrieved calculated decay constant based on the performed nonlinear regression fitting from time 0 to a future time point; and (c) calculating a mean confidence interval of the TOF curve.

15. 10. The method of claim 1, further comprising staining the biological specimen for the presence of at least one biomarker.

16. 16. The method of claim 15, wherein the at least one biomarker is a cancer biomarker.

17. 16. The method of claim 15, further comprising scoring the stained biological specimen for the presence of the at least one biomarker.

18. 10. The method of claim 1, further comprising staining the biological specimen for the presence of at least two biomarkers.

19. 1. A method for predicting time to completion of fixation of a biological specimen immersed in one or more fixatives, comprising: (a) acquiring acoustic data at one or more locations along the biological specimen immersed in the one or more fixatives (401); (b) deriving time-of-flight (TOF) data from the acquired acoustic data, the derived TOF data including one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated attenuation constants (402); (c) simultaneously calculating at least two reliability models based on the derived TOF data, the at least two reliability models including a historical reliability model, a current reliability model, and / or a future reliability model (403); (d) determining (404) when the at least two calculated reliability models each independently meet a predetermined threshold criterion; (e) predicting the time to fixation completion based on the TOF data corresponding to the determined time point at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion (405); Including, the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test; The method, wherein performing the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

20. 20. The method of claim 19, wherein the biological specimen is first immersed in one or more fixatives at a temperature of less than about 15°C.

21. 21. The method of any one of claims 19 and 20, wherein the acoustic data is acquired after the one or more fixatives are warmed to a temperature greater than about 15°C.

22. 21. The method of any one of claims 19 and 20, wherein the acoustic data is acquired after the one or more fixatives are warmed to a temperature greater than about 25°C.

23. 21. The method of claim 19, wherein the calculated at least two confidence models include the historical confidence model and the current confidence model.

24. 21. The method of claim 19, wherein the calculated at least two confidence models include the past confidence model, the current confidence model, and the future confidence model, respectively.

25. 1. A non-transitory computer-readable medium storing instructions for estimating an optimal diffusion time of a fluid into a biological specimen immersed in the fluid, the non-transitory computer-readable medium comprising: a. deriving time-of-flight (TOF) data from the acquired acoustic data; b. simultaneously calculating at least two confidence models based on the derived TOF data, wherein the at least two confidence models include a historical confidence model, a current confidence model, and / or a future confidence model; c. determining when the at least two calculated reliability models each independently meet a predetermined threshold criterion; d. estimating the time for optimal diffusion of the fluid into the biological specimen based on the TOF data corresponding to the determined time points at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion; Including, the non-transitory computer-readable medium further comprising instructions for performing a convergence test; The non-transitory computer-readable medium, wherein the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

26. 26. The non-transitory computer-readable medium of claim 25, further comprising instructions for calculating one or more decay constants.

27. A non-transitory computer-readable medium storing instructions for estimating a time for optimal diffusion of a fluid into a biological specimen immersed in the fluid, comprising: (a) deriving TOF data from acoustic data acquired from the biological specimen immersed in the fluid; (b) continuously calculating past reliability models, current reliability models, and future reliability models until each of the past, current, and future reliability models simultaneously and independently satisfy a predetermined threshold criterion; (c) identifying time points corresponding to the derived TOF data at which the past, current, and future reliability models are simultaneously and independently satisfied; and (d) estimating the time for optimal diffusion of the fluid into the biological specimen based on the TOF data corresponding to the identified time points; the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test; The non-transitory computer-readable medium, wherein performing the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

28. 28. The non-transitory computer-readable medium of claim 27, wherein the average attenuation constant is derived by (i) taking a calculated attenuation constant for each of a predetermined number of TOF data points preceding the candidate TOF data point, and (ii) averaging each of the taken calculated attenuation constants.

29. 29. The non-transitory computer-readable medium of claim 27, wherein the calculated current confidence model satisfies a predetermined current confidence model threshold criterion when the calculated current confidence interval is below a predetermined current confidence model threshold.

30. 30. The non-transitory computer-readable medium of claim 29, wherein the current confidence interval is calculated by: (a) performing a non-linear regression fit of the TOF curve using some or all of the calculated TOF data points of the calculated TOF curve; and (b) determining the confidence interval of the retrieved calculated decay constant based on the performed non-linear regression fitting.

31. 28. The non-transitory computer-readable medium of claim 27, wherein the calculated future confidence model satisfies a predetermined future confidence model threshold criterion when a calculated future confidence interval is below a predetermined future confidence model threshold.

32. 32. The non-transitory computer-readable medium of claim 31 , wherein the future confidence interval is calculated by: (a) performing a non-linear regression fit of the TOF curve using all of the calculated TOF data points of the calculated TOF curve; (b) determining the confidence interval of the retrieved calculated decay constant based on the performed non-linear regression fitting from time 0 to a future time point; and (c) calculating a mean confidence interval of the TOF curve.

33. 30. The non-transitory computer-readable medium of claim 27, wherein the fluid comprises one or more fixatives.

34. 28. The non-transitory computer-readable medium of claim 27, wherein the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

35. 1. A system (200) for estimating an optimal diffusion time of a fluid into a biological specimen immersed in the fluid, comprising: (i) one or more processors (206); and (ii) one or more memories (205) coupled to the one or more processors (206), the one or more memories (205) storing computer-executable instructions that, when executed by the one or more processors (206), cause the system (200) to: (a) deriving time-of-flight (TOF) data from acoustic data acquired at one or more positions along a biological specimen immersed in a fluid; (b) simultaneously calculating at least two reliability models based on the derived TOF data, the at least two reliability models including a historical reliability model, a current reliability model, and / or a future reliability model; (c) determining when the at least two calculated reliability models each independently meet a predetermined threshold criterion; (d) estimating an optimal diffusion time of the fluid into the biological specimen based on the TOF data corresponding to the determined time points at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion; and Execute an operation including the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if the one or more processors (206) determine that a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are each within a predetermined threshold percentage value of an average calculated attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test by the one or more processors (206); The system, wherein performing the convergence test includes: (i) calculating a candidate attenuation constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average attenuation constant based on a predetermined number of calculated attenuation constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

36. 36. The system of claim 35, wherein the fluid comprises one or more fixatives.

37. 36. The system of claim 35, wherein the fluid is selected from the group consisting of ethanol, xylene, and paraffin.

38. 1. A method for estimating the optimal diffusion time of one or more fixatives in a biological specimen immersed in a fluid, comprising: (a) immersing the biological specimen in the one or more fixatives, wherein the one or more fixatives are maintained at a temperature of less than about 15°C; (b) acquiring acoustic data at one or more positions along the biological specimen immersed in the fluid (401); (c) deriving time-of-flight (TOF) data from the acquired acoustic data, the derived TOF data including one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated attenuation constants (402); (d) simultaneously calculating at least two reliability models based on the derived TOF data, the at least two reliability models including a historical reliability model, a current reliability model, and / or a future reliability model (403); (e) determining when the at least two calculated reliability models each independently meet a predetermined threshold criterion (404); (f) estimating (405) an optimal diffusion time for the fluid in the biological specimen based on the TOF data corresponding to the determined time points at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion; Including, the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test; The method, wherein performing the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

39. 39. The method of claim 38, wherein the temperature is less than about 10°C.

40. 39. The method of claim 38, wherein the temperature ranges from about -10°C to about 10°C.

41. 39. The method of claim 38, wherein the temperature ranges from about -4°C to about 4°C.

42. 1. A method for predicting time to completion of fixation of a biological specimen immersed in one or more fixatives, comprising: (a) immersing the biological specimen in the one or more fixatives, wherein the one or more fixatives are maintained at a temperature greater than about 20°C; (b) acquiring acoustic data at one or more locations along the biological specimen immersed in the one or more fixatives; (c) deriving time-of-flight (TOF) data from the acquired acoustic data, the derived TOF data including one or more calculated TOF data points, one or more calculated TOF curves, and / or one or more calculated attenuation constants; (d) simultaneously calculating at least two reliability models based on the derived TOF data, the at least two reliability models including a historical reliability model, a current reliability model, and / or a future reliability model; (e) determining when the at least two calculated reliability models each independently meet a predetermined threshold criterion; (f) predicting the time to fixation completion based on the TOF data corresponding to the determined time point at which the calculated at least two reliability models each independently satisfied the predetermined threshold criterion; Including, the calculated historical reliability model satisfies a predetermined historical reliability model threshold criterion if a predetermined number of retrieved calculated candidate attenuation constants corresponding to a plurality of derived TOF data points are determined to be each within a predetermined threshold percentage value of the calculated average attenuation constant; determining whether the predetermined number of retrieved calculated candidate decay constants are within the predetermined threshold percentage value of the calculated average decay constant includes performing a convergence test; The method, wherein performing the convergence test includes: (i) calculating a candidate decay constant for a candidate calculated TOF data point to provide a theoretical value; (ii) calculating an average decay constant based on a predetermined number of calculated decay constants calculated over time and corresponding to a predetermined number of TOF data points preceding the candidate TOF data point to provide an experimental value; (iii) determining an actual percent error based on the calculated theoretical value and the calculated experimental value; and (iv) comparing the determined percent error to a predetermined threshold percentage value.

43. 43. The method of claim 42, wherein the temperature is greater than about 25°C.

44. 43. The method of claim 42, wherein the temperature is greater than about 30°C.

45. 43. The method of claim 42, wherein the temperature is greater than about 35°C.

46. 43. The method of claim 42, wherein the temperature is greater than about 40°C.

47. 43. The method of claim 42, wherein the temperature is greater than about 45°C.

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