Systems and methods for sample processing

Abstract

Methods and systems for processing tissues according to a specific processing protocol established based on time-of-flight measurements of the diffusion of a processing fluid into a tissue sample are described. In one embodiment, a measurement of the time taken for approximately 70% of the ethanol to diffuse into the tissue sample is used to predict the time it would take to diffuse other processing fluids into the same or similar tissue samples. Advantageously, the disclosed methods and systems can reduce overall processing time and help ensure that only samples requiring similar processing conditions are batch-processed together.

Description

[0001] This application is a divisional application of application number 201780079550.9 filed on December 21, 2017, entitled "System and Method for Sample Processing".

[0002] Cross-reference to related applications

[0003] This disclosure claims the benefit of U.S. Provisional Patent Application No. 62 / 438,152, filed December 22, 2016, and U.S. Provisional Patent Application No. 62 / 437,962, filed December 22, 2016, both of which are incorporated herein by reference in their entirety. Technical Field

[0004] This disclosure generally relates to systems and methods for ensuring that biological samples are properly processed for analysis. In particular, this disclosure relates to systems and methods for ensuring that cell samples are properly processed for microscopic analysis. Background Technology

[0005] Proper histological staining of tissue biopsies remains the gold standard for clinical diagnosis. Before tissue samples can be examined using optical microscopy, they must first be fixed, processed, and sectioned into thin sections. These sections are placed on microscope slides and stained to enhance morphological features to highlight the presence of diagnostic markers. Current clinical practice involves placing the sample in a fixative (such as neutral-buffered formalin (NBF)) after the procedure. After diffusing into the tissue, formaldehyde stabilizes the tissue through cross-linked biological structures within it and prevents further degradation. This hardens the sample for downstream sectioning and protects it from subsequent processing chemicals. Next, the sample is dehydrated by a series of gradients of ethanol with increasing concentrations, which gradually remove all aqueous solutions from the tissue. Finally, because paraffin and ethanol are largely immiscible, the tissue is placed in a series of scavenging agents (such as xylene) to remove the ethanol and prepare the sample for infiltration with embedding reagents. The most common embedding reagent is heated liquid paraffin, which permeates into the sample and is then rapidly cooled to provide a supporting structure for sectioning and for long-term preservation of the sample.

[0006] Although the fixation and handling of histological specimens aim to preserve the tissue in a state that indicates how it was removed from the test sample, these steps are not without consequences. Incomplete ethanol dehydration or xylene removal will impair the ability of paraffin to penetrate the sample, making the specimen soft and difficult to cut. Conversely, overexposure to dehydrating and purging chemicals can make the tissue stiff and brittle, leading to sectioning artifacts such as tissue tearing, micro-tremors, or the so-called "Venetian blind" effect. Strong dehydrating chemicals, such as ethanol, work by removing free water from biomolecules and removing bound water molecules. However, because water isolates these molecules from each other, when it is removed, neighboring molecules can be attracted together by electrostatic forces, and as a result, the tissue shrinks, causing morphological distortion. Care must be taken to gradually increase the concentration of the ethanol solution, or distortions may also occur at the cell membrane. Short processing protocols or, alternatively, the use of depleted reagents can also lead to the loss of structural detail.

[0007] Significant evidence exists that the choice of fixative, fixative temperature, duration of fixation, and other preanalytical variables (such as hot and cold local ischemia) can significantly alter the antigenicity of a sample. Unfortunately, the impact of subsequent processing steps on tissue integrity and antigen retrieval is significantly less understood. However, there is growing evidence that processing steps can indeed affect staining quality. For example, incomplete and excessive dehydration has been associated with weak immune recognition of target cells and increased background signaling, while complete dehydration and infiltration result in improved staining for several targets (such as L26 and Kappa). In addition to reagent temperature, the specific reagents used for dehydration and removal can also affect the intensity and universality of immunohistochemical (IHC) staining. Several studies have reported superior RNA and protein preservation with prolonged dehydration times. Even paraffin has been reported to affect staining quality, as low-temperature paraffin has been reported to produce superior IHC results and less fragmented RNA.

[0008] Furthermore, the processing steps appear to be a disruptive factor in antigen retrieval, and there seems to be a synergistic effect between how completely the sample is fixed and the extent to which it is processed. For example, in the presence of ethanol, ribonuclease A molecules collapse from their native α+β protein conformation to a pure β state, and this conformational change can indeed be important for antigen retrieval. This finding also seems to be supported by the fact that lightly treated samples are more receptive to heat-induced antigen retrieval. Ethanol acts to remove N-hydroxymethyl adducts before they have a chance to form stable crosslinks by binding to another amine. Thus, in the case of hastily or poorly crosslinked samples, ethanol can act to prevent complete crosslinking and stabilization of the sample. Poorly fixed tissues are also particularly susceptible to mutation when ethanol removes bound water molecules, causing a hydrophobic reversal of the tissue's biological structure, leading to problems such as morphological distortion, faded nuclei, poor nuclear chromatin patterns, and abnormal staining of collagen. Conversely, properly crosslinked samples are less prone to distortion during processing.

[0009] Current clinical tissue processing occurs in batch mode using tissue processors that simultaneously fix and process dozens of tissue cartridges. Clinical specimens are typically grouped together, with all core biopsies processed via a rapid protocol and larger samples processed via separate, longer protocols. This batch processing exposes rapidly diffused samples to excessive exposure to dehydrating and cleansing chemicals, while slowly diffused tissues risk underprocessing, especially with rapid protocols. Furthermore, fixation and processing are slow processes, often a bottleneck in the pre-analysis phase. For example, small 3mm biopsies are recommended to undergo 12 hours for optimal processing, while larger specimens fitting standard-sized histology cartridges may require several days to be optimally prepared for sectioning and other downstream staining tests. Additionally, batch processing times are highly variable and largely lab-specific, which can lead to variations in pre-analysis based on the tissue processing steps. Techniques that accelerate the processing of histology specimens without compromising tissue or biomarker quality will be widely applicable in current practice.

[0010] Currently, no technology can quantify, statically or dynamically, the extent to which a sample is dehydrated, removed, or embedded. Summary of the Invention

[0011] This document discloses systems and methods for time-of-flight (TOF) monitoring of the diffusion rates of tissue processing fluids (e.g., gradients of ethanol and xylene, and formalin) into several different tissue types. The disclosed systems and methods can be used to measure, monitor, and / or predict the extent to which samples are dehydrated, cleaned, and embedded to minimize the detrimental effects of such processes on downstream immune recognition, such as overall tissue quality and biomarkers. Furthermore, in some embodiments, the disclosed systems and methods can be used to expedite the time required to process tissues and provide guidance for standardizing the processing of histological specimens in a fully traceable and / or reproducible pre-analysis workflow.

[0012] Surprisingly, it has been shown that, based on the rate of diffusion of 70% ethanol into a sample (such as by measurement, by a diffusion rate constant as discussed below, or by prediction of the time taken to achieve a 70% ethanol concentration at a specific point in the sample (such as at the center of the sample), which is also disclosed below), one can accurately predict the diffusion rate of other chemicals used for tissue treatment.

[0013] One aspect of this disclosure is a method for processing tissue, comprising: subjecting a first tissue sample to TOF analysis while the first tissue sample is immersed in a first processing fluid; determining a first processing time sufficient for diffusion of a predetermined amount of the first processing fluid into the first tissue sample; determining a second processing time sufficient for diffusion of a predetermined amount of a second processing fluid into the first sample; wherein the second processing time is calculated based on the first processing time and a predetermined functional relationship between the first and second processing times; and immersing the first tissue sample in the second processing fluid for the second processing time.

[0014] In some embodiments, determining a sufficient first processing time for diffusion of a predetermined amount of the first processing fluid into a first tissue sample includes determining one or more of the following: (i) the time taken to observe a predetermined change in the decay time of a measured TOF signal of the first sample while the first sample is immersed in the first processing fluid; (ii) the time taken to observe a predetermined change in the attenuation amplitude of a measured TOF signal of the first sample while the first sample is immersed in the first processing fluid; (iii) the time taken to observe a predetermined change in the percentage diffusion calculated based on the measured TOF signal of the first sample while the first sample is immersed in the first processing fluid; and (iv) the time taken to observe a predetermined change in the reagent concentration at the tissue center calculated based on the measured TOF signal of the first sample while the first sample is immersed in the first processing fluid.

[0015] In some embodiments, the first treatment fluid comprises approximately 70% ethanol, and the second treatment fluid comprises one of the following: approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin. In some embodiments, the first treatment time is used to determine a second treatment time for a plurality of second treatment fluids. In some embodiments, the first treatment fluid comprises approximately 70% ethanol, and a second treatment time is determined for at least approximately 90% ethanol and xylene, and the method further comprises immersing a first tissue sample in approximately 90% ethanol for the determined second treatment time, and immersing the first tissue sample in xylene for the determined second treatment time. In some embodiments, the first treatment fluid comprises approximately 70% ethanol, and a second treatment time is determined for each of approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin, and the method further comprises successively immersing the first tissue sample in approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin for the determined second treatment times for approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin, respectively.

[0016] In some embodiments, the method may further include selecting a second tissue sample of a certain type, shape, and size having diffusion properties substantially similar to those of the first tissue sample, and immersing the second tissue sample in a first processing fluid for a first processing time and in a second processing fluid for a second processing time.

[0017] In some embodiments, the method further includes: subjecting the first tissue sample to TOF analysis while it is immersed in the first processing fluid, and determining a first processing time sufficient for the diffusion of a predetermined amount of the first processing fluid into the first tissue sample, wherein the steps are performed across multiple tissue types, multiple tissue sizes, and multiple tissue shapes to provide a lookup table of first processing times for tissue samples of a particular type, size, and shape. Once the lookup table is established, in some embodiments, the method may further include: selecting a second tissue sample and selecting a first processing time for the second tissue sample from the lookup table, wherein the selection of the first processing time is based on the type, size, and shape of the second tissue sample. Alternatively, and again based on the use of the established lookup table, the method may further include batch processing two or more second tissue samples together for processing based on two or more second tissue samples having substantially similar first processing times. As used herein, “substantially similar” means parameters (such as time, size, shape, diffusion characteristics, processing time, protocol, etc.) within ±20% of each other. For example, if the treatment time in a particular treatment fluid (such as 70% ethanol) differs between two protocols by less than approximately ±20% (such as less than approximately ±10% or less than approximately ±5%), then the two tissue samples may have substantially similar treatment protocols.

[0018] In another aspect of this disclosure is a system comprising a tissue processing system, a non-transitory memory, and a processor communicatively coupled to the tissue processing system and the non-transitory memory. The memory may include a database of protocol instructions stored therein, the database including tissue processing steps and other data, such as time or timing, for tissue samples of a specific type, shape, and / or size, and / or for one or more groups of tissue samples of a specific type, size, and / or shape that share generally similar processing protocols. The system may include a user interface providing a data entry function for a user, wherein the data entry function allows the user to enter the type, shape, and size of a tissue sample, or select the group to which the tissue sample of said type, shape, and size belongs, wherein when entering the type, size, and shape of a tissue sample or selecting the group to which the tissue sample of said type, shape, and size belongs, the processor controls the tissue processing system to process the tissue according to the protocol stored in the database for the entered tissue type, shape, and size or the selected group. In some embodiments, user input of the type, shape, and size of tissue samples causes the processor to retrieve groups of tissue samples of a specific type, size, and shape that share substantially the same processing protocol as the tissue samples and display these groups to the user on a user interface. In some embodiments, the system is configured to process multiple different samples and / or multiple different groups of samples sharing substantially similar processing protocols in parallel and according to different protocol instructions.

[0019] Another aspect of this disclosure is a time-of-flight (TOF) tissue processing system. The time-of-flight (TOF) tissue processing system includes: a first saturation tank in which a tissue sample can be immersed in a first processing fluid; an acoustic monitoring device configured to acquire TOF data from the tissue sample while it is immersed in the first processing fluid; a processor configured to receive the TOF data and calculate a first processing time sufficient for the diffusion of a predetermined amount of the first processing fluid into the tissue sample, the processor further configured to calculate a second processing time sufficient for the diffusion of a predetermined amount of the second processing fluid into the tissue sample, wherein the second processing time is calculated based on the first processing time and a predetermined functional relationship between the first and second processing times; and a second saturation tank in which the tissue sample is immersed in a second processing fluid, wherein the processor monitors the time the tissue sample is immersed in the second processing fluid and, when the second processing time is reached, either warns the user to remove the tissue sample from the second saturation tank or causes the system to automatically remove the tissue sample from the second processing fluid in the second saturation tank. In some embodiments, the first treatment fluid is approximately 70% ethanol, and the second treatment fluid is one of approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin. In some embodiments, calculating a first treatment time sufficient for the diffusion of a predetermined amount of the first treatment fluid into the tissue sample includes determining one or more of the following: the time taken to observe a predetermined change in the decay time of the measured TOF signal of the first sample while the first sample is immersed in the first treatment fluid; the time taken to observe a predetermined change in the decay amplitude of the measured TOF signal of the first sample while the first sample is immersed in the first treatment fluid; the time taken to observe a predetermined change in the percentage diffusion calculated based on the measured TOF signal of the first sample while the first sample is immersed in the first treatment fluid; and the time taken to observe a predetermined change in the reagent concentration at the tissue center calculated based on the measured TOF signal of the first sample while the first sample is immersed in the first treatment fluid. Attached Figure Description

[0020] Figure 1 An exemplary embodiment of the present disclosure is shown, comprising an organization processing system 100 for optimized organization fixation.

[0021] Figure 2A and 2B The images depict ultrasound scan patterns from a biopsy capsule and a standard-sized cartridge, respectively.

[0022] Figure 2C A timing diagram of an exemplary embodiment of the present disclosure is shown.

[0023] Figure 3 A method for obtaining a diffusion coefficient for a tissue sample is shown according to an exemplary embodiment of the present disclosure.

[0024] Figure 4 An alternative method for obtaining the diffusivity coefficient for a tissue sample is shown.

[0025] Figures 5A-5B Simulated concentration gradients are shown for the first time point and for several time points in the experimental process.

[0026] Figure 6A and 6B The detected NBF concentration was plotted using ultrasound during the experimental process and a simulated TOF signal for the first candidate diffusion constant.

[0027] Figure 7 The time-varying TOF signals calculated for all potential diffusion rate constants are depicted.

[0028] Figure 8A and 8B The experimentally calculated TOF trend and the spatially averaged TOF signal collected from a 6 mm fragment of a human tonsil sample were depicted, respectively.

[0029] Figure 9A and 9B Plots and magnified views of the calculated error function as a function of the candidate diffusion rate constant between simulated and experimentally measured TOF signals are shown.

[0030] Figure 10 The TOF trend calculated using the diffusion rate constant modeled next to the experimental TOF is depicted.

[0031] Figure 11A and 11B The reconstructed diffusion rate constants for multiple tissue samples are shown.

[0032] Figure 12A-12B A system is shown that includes a transmitter and receiver pair for measuring TOF via phase shift.

[0033] Figure 13 A model of reagent diffusion into cylindrical objects, such as cylindrical tissue cores, is shown.

[0034] Figure 14 The typical distribution of the percentage diffusion of the reagent into the center of the tissue sample is shown at approximately 3 hours and approximately 5 hours.

[0035] Figure 15Typical ROC curves for staining quality (based on sensitivity and specificity) based on percentage diffusion at the center of the tissue sample are shown.

[0036] Figure 16 A typical graph shows the difference in percentage diffusion at the center of a tissue sample between approximately 3 hours and approximately 5 hours after exposure to the reagent.

[0037] Figure 17 The raw data distribution of amygdala tissue volume porosity determined according to the disclosed embodiments is shown.

[0038] Figure 18 The distribution of whisker pattern for the volumetric porosity of tonsillar tissue, as determined according to the disclosed embodiments, is shown.

[0039] Figure 19 Typical distributions of formaldehyde concentration at the center of a tissue sample, as determined according to the disclosed examples, are shown at approximately 3 hours and approximately 5 hours.

[0040] Figure 20 Typical ROC curves for staining quality (based on sensitivity and specificity) based on formaldehyde concentration at the center of the tissue sample are shown.

[0041] Figure 21 A typical graph shows the difference in formaldehyde concentration at the center of a tissue sample between approximately 3 hours and approximately 5 hours of immersion in NBF solution.

[0042] Figure 22 The distribution of original porosity for several tissue types, as determined according to the disclosed embodiments, is shown.

[0043] Figure 23 A set of whisker distributions for porosity for several tissue types, as determined according to the disclosed embodiments, are shown.

[0044] Figure 24 The distribution of diffusion rate constants for several tissue types is shown.

[0045] Figure 25 A set of frame distributions for the diffusion rate constants for several tissue types is shown.

[0046] Figure 26 The distribution of the original percentage diffusion at the center of the tissue sample at approximately 3, 5, and 6 hours is shown for several tissue types.

[0047] Figure 27 A set of frame distributions are shown for the percentage diffusion at the center of tissue samples at approximately 3, 5, and 6 hours for several tissue types.

[0048] Figure 28 The distribution of raw formaldehyde concentration at the center of tissue samples at 3, 5, and 6 hours is shown for several tissue types.

[0049] Figure 29 A set of frame distributions of formaldehyde concentrations at the center of tissue samples at approximately 3, 5, and 6 hours are shown for several tissue types.

[0050] Figure 30 The distribution of the original formaldehyde concentration at the center of the tissue sample for several tissue types is shown after the indicated immersion time.

[0051] Figure 31 A set of frame distributions of formaldehyde concentration at the center of tissue samples for several tissue types are shown after the indicated immersion time.

[0052] Figure 32 It shows Figure 31 The labeled version categorizes tissue types that provide optimal staining after immersion in 10% NBF for approximately 5 or 6 hours.

[0053] Figure 33 The original distribution of formaldehyde concentration at the center of the tissue sample is shown for all tissues after approximately 6 hours of immersion in 10% NBF.

[0054] Figure 34 The frame distribution of formaldehyde concentration at the center of the tissue sample is shown for all tissues after approximately 6 hours of immersion in 10% NBF.

[0055] Figure 35 A method for obtaining the diffusivity coefficient, porosity, and formaldehyde concentration at the center of a tissue sample, according to an exemplary embodiment of the present disclosure, is shown.

[0056] Figure 36A A perspective view of a tissue processing system with TOF enabled is shown.

[0057] Figure 36B It shows Figure 36A A perspective diagram of an acoustic monitoring device.

[0058] Figure 36C A tissue processing cartridge that holds several tissue fragments is shown.

[0059] Figure 36D Several TOF traces for a single tissue fragment are shown, collected by moving the sample relative to the transmitter and receiver portions of an acoustic monitoring device.

[0060] Figure 36EThe spatially averaged TOF signal (solid line) observed for the sample and its exponential curve fit (dashed line) are shown.

[0061] Figure 37A The raw (upper screen) and spatially averaged (lower screen) TOF signals are shown for a kidney sample of approximately 6 mm immersed in 10% NBF.

[0062] Figure 37B The raw (upper screen) and spatially averaged (lower screen) TOF signals are shown for a kidney sample of approximately 6 mm immersed in 70% EtOH.

[0063] Figure 37C The raw (upper screen) and spatially averaged (lower screen) TOF signals for a 6 mm kidney sample immersed in 90% EtOH are shown.

[0064] Figure 37D The raw (upper screen) and spatially averaged (lower screen) TOF signals are shown for a kidney sample of approximately 6 mm immersed in 100% EtOH.

[0065] Figure 37E The raw (upper screen) and spatially averaged (lower screen) TOF signals are shown for a kidney sample of approximately 6 mm immersed in xylene.

[0066] Figure 37F The average signal error of all samples monitored across each treatment solution, as shown on the x-axis, is presented.

[0067] Figure 37G The R-squared average of all samples monitored across each treatment solution, as shown on the x-axis, is presented.

[0068] Figure 37H The signal-to-noise ratio is shown as the average of all samples monitored across each treatment solution, as shown on the x-axis.

[0069] Figure 38A The absolute TOF signals for normal kidney tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0070] Figure 38B The absolute TOF signals for normal breast tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0071] Figure 38CThe absolute TOF signals for normal colonic tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0072] Figure 38D The absolute TOF signal for renal cell carcinoma tissue over time is shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area represents ±σ.

[0073] Figure 38E The absolute TOF signal for breast cancer tissue over time is shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0074] Figure 38F The absolute TOF signals for adipose tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0075] Figure 39A Normalized TOF signals for normal kidney tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The black solid line represents the mean signal, and the shaded area represents ±σ.

[0076] Figure 39B Normalized TOF signals for normal breast tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The black solid line represents the average signal, and the shaded area represents ±σ.

[0077] Figure 39C Normalized TOF signals for normal colonic tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The black solid line represents the average signal, and the shaded area represents ±σ.

[0078] Figure 39D Normalized TOF signals for renal cell carcinoma tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The black solid line represents the mean signal, and the shaded area represents ±σ.

[0079] Figure 39EThe normalized TOF signal for breast cancer tissue over time is shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area represents ±σ.

[0080] Figure 39F The normalized TOF signals for adipose tissue over time are shown in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The black solid line represents the average signal, and the shaded area represents ±σ.

[0081] Figure 40A The distribution of all attenuation magnitudes by reagent group is shown. The solid line is the median, the solid box represents 25% to 75%, the must be extended to 1.5 times the interquartile range, and data outside the must be represented by circles.

[0082] Figure 40B The distribution of attenuation magnitudes by reagent and tissue type is shown. Negative magnitudes represent increasing TOF, and positive magnitudes represent decreasing TOF. The solid line is the median, and the solid box represents 25% to 75%, extending to 1.5 times the interquartile range. Data outside the whiskers are indicated by circles. Ca = cancer.

[0083] Figure 40C The distribution of time required for 90% diffusion by reagent group is shown. The solid line is the median, the solid box represents 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles.

[0084] Figure 40D This shows the distribution of time required for 90% diffusion of the reagent and tissue type. The solid line is the median, the solid box represents 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles. Ca = cancer.

[0085] Figure 41A The diffusion times for 90% ethanol versus 70% ethanol are shown for several tissue types as indicated. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis, with specific tissue types labeled accordingly.

[0086] Figure 41B The diffusion times for 90% ethanol versus 100% ethanol are shown for several tissue types as indicated. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis.

[0087] Figure 41CThe diffusion times of xylene versus 70% ethanol for several tissue types as indicated are shown. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis.

[0088] Figure 42 The plot shows the time of pairwise data for 70% ethanol to 90% ethanol to achieve 90% diffusion, as well as the power regression fit (solid line) and its 99% prediction interval (dashed line).

[0089] Figure 43A , 43B 43C and 43D show examples such as those targeting Figure 42 The regression shown is a statistical measure of the quality of fit.

[0090] Figure 44 The plot shows the time of pairwise data for 70% ethanol to approximately 100% ethanol to reach 90% diffusion, as well as the power regression fit (solid line) and its 99% prediction interval (dashed line).

[0091] Figure 45A , 45B 45C and 45D show examples of what is intended for use with respect to... Figure 44 The regression shown is a statistical measure of the quality of fit.

[0092] Figure 46 The plot shows the time of pairwise data for achieving 90% diffusion of 70% ethanol to absolute xylene, as well as the power regression fit (solid line) and its 99% prediction interval (dashed line).

[0093] Figure 47A , 47B 47C and 47D show examples of what is intended for use with respect to... Figure 46 The regression shown is a statistical measure of the quality of fit. Detailed Implementation

[0094] It should also be understood that, unless clearly indicated to the contrary, in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order used to describe the steps or actions of the method.

[0095] As used herein, the singular terms “a,” “one,” and “the” include plural references unless the context clearly indicates otherwise. Similarly, the word “or” is intended to include “and” unless the context clearly indicates otherwise. The term “including” is inclusive, such that “including A or B” means including A, B, or A and B.

[0096] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when items are separated in a list, “or” or “and / or” should be interpreted as inclusive, that is, including multiple elements or at least one of the elements in the list, but also including more than one, and optionally including additional unlisted items. The term “only” clearly indicates the opposite, such as “only one of…” or “exact one of…”, or when used in the claims, “consisting of…” will refer to including multiple elements or an exact one of the elements in the list. Generally, the term “or” as used herein, when followed by an exclusive term (such as “any one,” “one of…,” “only one of…,” or “exact one of…”), should be interpreted only to indicate an exclusive alternative (i.e., “one or the other but not both”). “Substantially consisting of…” when used in the claims should have its ordinary meaning as used in the field of patent law.

[0097] The terms “comprising,” “including,” “having,” etc., are used interchangeably and have the same meaning. Similarly, each of these terms is defined in accordance with the common U.S. patent law definition of “comprising” and is therefore interpreted as an open term meaning “at least the following,” and is also interpreted as not excluding additional features, limitations, aspects, etc. Thus, for example, “the apparatus has components a, b, and c” means that the apparatus includes at least components a, b, and c. Similarly, the phrase “the method involves steps a, b, and c” means that the method includes at least steps a, b, and c. Furthermore, although steps and processes may be outlined herein in a specific order, those skilled in the art will recognize that the order of steps and processes may vary.

[0098] As used herein in the specification and in the claims, the phrase "at least one" relating to a list of one or more elements should be understood to mean at least one element selected from any one or more elements in the list of elements, but does not necessarily include at least one of every element specifically listed in the list of elements and does not exclude any combination of elements in the list of elements. This definition also allows for the optional presence of elements other than those specifically identified in the list of elements referred to by the phrase "at least one," whether related to or unrelated to those specifically identified elements. 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") in one embodiment may refer to at least one, optionally including more than one A, where B is absent (and optionally including elements other than B); in another embodiment, it refers to at least one, optionally including more than one B, where no A is present (and optionally including elements other than A); in yet another embodiment, it refers to at least one, optionally including more than one A, and at least one, optionally including more than one B (and optionally including other elements); and so on.

[0099] I. Technical Implementation Methods

[0100] This disclosure provides systems and computer-implemented methods for reconstructing a spatiotemporal concentration profile across tissue samples using acoustic time-of-flight (TOF)-based information associated with a diffusion model to calculate a diffusivity constant (also known as a “diffusion coefficient”) and sample porosity. In some embodiments, the tissue preparation systems and methods disclosed herein can be adapted to monitor the diffusion of fixative fluids into tissue samples until a predetermined concentration level is reached. For example, as formalin penetrates into the tissue, it displaces interstitial fluids. This fluid exchange at least partially alters the composition of the tissue volume, and this change can be monitored. As an example, assuming that the interstitial fluids and formalin each respond differently to an introduced ultrasonic pulse (i.e., each fluid has a discrete “velocity of sound” property), the output ultrasonic pulses will accumulate small travel time differences that increase as more fluid exchange occurs (i.e., as more formalin displaces the interstitial fluids). This enables operations such as: determining the phase difference accumulated by diffusion based on the geometry of the tissue sample, modeling the effect of diffusion on TOF, and / or using post-processing algorithms to correlate the results to determine the diffusivity constant. Furthermore, the disclosed TOF instrument has the sensitivity to detect changes of less than 10 parts per million, potentially enabling more accurate characterization of diffusivity constants and porosity. At the nanosecond TOF scale, all fluids and tissues will have discrete sound velocities, therefore the disclosed operation is not limited to quantifying only water diffusion, but can be used to monitor the diffusion of all fluids into all tissues. For example, the diffusion of dehydrating agents (such as gradient ethanol), scavenging agents (such as xylene), and paraffin used to embed tissue samples.

[0101] Diffusion rates can be monitored by an acoustic detector system based on the different acoustic properties of formalin-soaked tissue samples. Such systems for diffusion monitoring and experimental TOF measurements are described in further detail in U.S. patent publications 2013 / 0224791, 2017 / 0284969, 2017 / 0336363, 2017 / 0284920, and 2017 / 0284859, the contents of which are each incorporated herein by reference in their entirety. Another suitable system for diffusion monitoring and experimental TOF measurements is described in International Patent Application filed December 17, 2015, entitled ACCURATELY CALCULATINGACOUSTIC TIME-OF-FLIGHT, the contents of which are hereby incorporated herein by reference in their entirety.

[0102] Further examples of suitable systems and methods for TOF monitoring are described in PCT Publication No. WO2016 / 097163 and U.S. Patent Publication No. 2017 / 0284859, the contents of which are incorporated herein by reference to the extent that they are not inconsistent with the contents of this disclosure. The referenced applications describe contact between a solid tissue sample and a fluid fixative, the fluid fixative traveling through the tissue sample and spreading substantially throughout the entire thickness of the tissue sample, and analysis of the solid tissue sample based on acoustic properties that are continuously or periodically monitored to assess the state and condition of the tissue sample throughout the treatment. For example, fixatives having a larger bulk modulus than the interstitial fluid (such as formalin) can significantly alter the TOF as they displace the interstitial fluid. Based on the information obtained, fixation protocols can be adjusted to enhance treatment consistency, reduce treatment time, improve treatment quality, etc. Acoustic measurements can be used for non-invasive analysis of tissue samples. The acoustic properties of the tissue sample can change as a fluid reagent (e.g., a fluid fixative) travels through the sample. The acoustic properties of a sample can change during processes such as pre-soaking (e.g., diffusion of a cold fixative), fixation, staining, etc. During fixation (e.g., cross-linking), the rate of acoustic energy transfer can change as the tissue sample becomes more extensively cross-linked. Real-time monitoring can be used to accurately track the movement of the fixative through the sample. For example, the diffusion or fixation status of a biological sample can be monitored based on the time-of-flight (TOF) of sound waves. Other examples of measurements include acoustic signal amplitude, attenuation, scattering, absorption, phase shift of sound waves, or combinations thereof.

[0103] In other embodiments, the movement of the fixative through the tissue sample can be monitored in real time.

[0104] II. Systems and Methods

[0105] As used herein, the term "time of flight" or "TOF" refers to the time, for example, for an object, particle, or sound wave, electromagnetic wave, or other wave, to travel a distance through a medium. TOF can be measured empirically, for example, by determining the phase difference between the phase of the acoustic signal emitted by the transmitter ("transmitted signal") and the phase of the acoustic signal received by the receiver that has passed through an object immersed in fluid ("received signal") and the phase of the acoustic signal that has passed through the fluid alone. As used herein, a "sample" is, for example, a biological specimen containing multiple cells. Examples include, but are not limited to, tissue biopsy samples, surgical specimen samples, amniocentesis samples, and post-mortem materials. A sample may be contained, for example, on a tissue slide.

[0106] As used herein, the terms “biological sample,” “biological specimen,” “tissue sample,” “sample,” etc., refer to any sample obtained from any organism, including viruses, comprising biological molecules such as proteins, peptides, nucleic acids, lipids, carbohydrates, or combinations thereof. Other examples of organisms include mammals (such as humans; veterinary animals such as cats, dogs, horses, cattle, and pigs; and laboratory animals such as mice, rats, and primates), insects, annelids, arachnids, marsupials, reptiles, amphibians, bacteria, and fungi. Biological samples include tissue samples (such as tissue fragments and needle biopsies of tissues), cell samples (such as cytological smears, such as Pap smears, or blood smears, or cell samples obtained by microdissection), or small portions, fragments, or organelles of cells (such as those obtained by lysing cells and separating their components by centrifugation or other methods). Other examples of biological samples include blood, serum, urine, semen, feces, cerebrospinal fluid, interstitial fluid, mucus, tears, sweat, pus, biopsy tissue (e.g., obtained by surgical or needle biopsy), nipple aspiration, earwax, milk, vaginal secretions, saliva, swabs (such as oral swabs), or any material containing biomolecules obtained from a first biological sample. In some embodiments, the term "biological sample," as used herein, refers to a sample (such as a homogenized or liquefied sample) prepared from a tumor or a portion of a tumor obtained from the test substance. The sample may be contained on, for example, a tissue sample slide.

[0107] The term "porosity" refers to a measure of the void (i.e., "empty") space in a material and is a fraction of the void volume to the total volume of the object, ranging between 0 and 1, or as a percentage between 0 and 100%. As used herein, "porous material" refers to, for example, a 3D object with a porosity greater than 0.

[0108] As used herein, the term "diffusion coefficient" or "diffusion constant" refers, for example, a proportionality constant between the molar flux due to molecular diffusion and the gradient (or driving force) of the concentration of the observed diffusing object. Diffusion rate is encountered in many equations in physical chemistry, such as Fick's law. The higher the difffusion rate (how much one substance diffuses relative to another), the faster the compound / object diffuses into each other. Typically, the difffusion constant of a compound in air is about 10,000 times greater than that in water. Carbon dioxide in air has a difffusion constant of approximately 16 mm. 2 The diffusivity constant is 0.0016 mm / s, and in water its diffusivity constant is approximately 0.0016 mm. 2 / s.

[0109] As used in this article, the phrase “phase difference” refers to, for example, the difference in degree or time between two waves having the same frequency and referenced to the same time point.

[0110] As used herein, the phrase "biopsy capsule" refers to, for example, a container for biopsy of tissue samples. Typically, a biopsy capsule comprises a grid for holding the sample and allowing liquid reagents (e.g., buffers, fixatives, or staining solutions) to surround and diffuse into the tissue sample. A biopsy capsule can maintain the sample in a specific shape that can advantageously provide a shape for the sample that is computationally easier to model according to the disclosed methods and thus more suitable for use in the disclosed systems. As used herein, "cassette" refers 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 such that it can be automatically selected and moved relative to the beam path of the ultrasound transmitter-receiver pair, e.g., raised and lowered, and the cassette also has an opening that allows liquid reagents to move into and out of the cassette, and thus further into and out of the tissue sample held therein. Movement can be performed, for example, by a robotic arm or another automated, movable component of a device on which the cassette is loaded. In other embodiments, the cartridge is used alone to contain the tissue sample, and the shape of the cartridge can at least partially determine the shape of the tissue sample. For example, placing a rectangular tissue block slightly thicker than the depth of the cartridge into the cartridge and closing the cartridge lid can cause the tissue sample to be compressed and stretched to fill a larger portion of the interior space of the cartridge, and thus transformed into a thinner piece with a greater height and width, but with a thickness roughly corresponding to the depth of the cartridge.

[0111] In some embodiments, a system for calculating formaldehyde or other reagents is disclosed, the system including a signal analyzer comprising a processor and a memory coupled to the processor, the memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform operations including calculating formalin concentration based on an acoustic dataset as described herein.

[0112] In some embodiments, the data input to the signal analyzer is an acoustic dataset generated by an acoustic monitoring system, which is generated by transmitting an acoustic signal such that the acoustic signal encounters material of interest, and then detecting the acoustic signal after the acoustic signal has encountered the material of interest. Thus, in some embodiments, a system is provided that includes a signal analyzer and an acoustic monitoring system. Additionally or alternatively, a system may include a signal analyzer and a non-transitory computer-readable medium, the non-transitory computer-readable medium including the acoustic dataset obtained from the acoustic monitoring system. In some embodiments, acoustic data is generated by a frequency sweep transmitted and received by the acoustic monitoring system. As used herein, the term "frequency sweep" refers to a series of sound waves transmitted through a medium at fixed frequency intervals, such that a first set of sound waves is emitted through the medium at a fixed frequency for a first fixed duration, and subsequent sets of sound waves are emitted at fixed frequency intervals for subsequent, preferably equal, durations.

[0113] In some embodiments, the system is adapted to monitor the diffusion of a fluid into a porous material. In some embodiments, a system may be provided comprising: (a) a signal analyzer; (b) an acoustic monitoring system and / or a non-transitory computer-readable medium, the non-transitory computer-readable medium including an acoustic dataset generated by the acoustic monitoring system; and (c) means for holding the porous material immersed in a fluid volume. In some embodiments, the system is adapted to monitor the diffusion of a fixative into a tissue sample.

[0114] In some embodiments, the concentration of formalin or other reagents is determined to characterize the extent to which the reagent has penetrated into the porous object. For example, the method can be used to monitor the coloring process of an object (e.g., fabric, plastic, ceramic, tissue, or others) to monitor fixation processes or other tissue treatment steps, such as dehydration, removal, and paraffin embedding.

[0115] In some embodiments, this disclosure provides an acoustic monitoring system for collecting acoustic datasets, wherein the acoustic monitoring system includes a transmitter and a receiver, wherein the transmitter and receiver are arranged such that acoustic signals generated by the transmitter are received by the receiver and converted into computer-readable signals. In an embodiment, the system includes 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 sound waves into electrical signals, and an “ultrasonic receiver” is a device capable of converting ultrasonic acoustic energy into electrical signals.

[0116] Some materials useful for generating acoustic energy from electrical signals are also useful for generating electrical signals from acoustic energy. Therefore, the transmitter and receiver do not necessarily need to be separate components, although they can be. The transmitter and receiver can be arranged such that the receiver detects the sound wave generated by the transmitter after the transmitted wave has encountered the material of interest. In some embodiments, the receiver is arranged to detect sound waves reflected by the material of interest. In other embodiments, the receiver is arranged to detect sound waves that have been transmitted through the material of interest.

[0117] In some embodiments, the transmitter includes at least a waveform generator operatively linked to a transducer configured to generate an electrical signal transmitted to the transducer, which is configured to convert the electrical signal into an acoustic signal. In some embodiments, the waveform generator is programmable, allowing a user to modify certain parameters of the sweep frequency, including, for example, start and / or end frequencies, step size between sweep frequencies, number of frequency steps, and / or duration of transmission for each frequency. In other embodiments, the waveform generator is pre-programmed to generate one or more predetermined sweep patterns. In other embodiments, the waveform generator may be configured to transmit both a pre-programmed sweep and a customized sweep. 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 region of an object.

[0118] In some embodiments, the transmitter may transmit a sweep frequency through a medium, which is then detected by a receiver and transformed into an acoustic dataset, which is stored in a non-transitory computer-readable storage medium and / or transmitted to a signal analyzer for analysis. Where the acoustic dataset includes data representing the phase difference between transmitted and received acoustic waves, the acoustic monitoring system may further include a phase comparator that generates an electrical signal corresponding to the phase difference between the transmitted and received acoustic waves. Thus, in some embodiments, the acoustic monitoring system includes a phase comparator communicatively linked to the transmitter and receiver. Where the output of the phase comparator is an analog signal, the acoustic monitoring system may further include an analog-to-digital converter (ADC) for converting the analog output of the phase comparator into a digital signal. The digital signal may then be recorded on, for example, a non-transitory computer-readable medium, or may be transmitted directly to a signal analyzer for analysis. Alternatively, the transmitter may transmit acoustic energy at a specific frequency, and the signal detected by the receiver is stored and analyzed for its peak intensity.

[0119] In some embodiments, a signal analyzer is provided, the signal analyzer including a processor and a memory coupled to the processor, the memory storing computer-executable instructions that, when executed by the processor, cause the processor to calculate formalin concentration based at least in part on an acoustic dataset generated by an acoustic monitoring system as discussed above.

[0120] The term "processor" includes all kinds of devices, apparatuses, and machines for processing data, including, for example, programmable microprocessors, computers, systems-on-a-chip, or a combination of the foregoing. The devices may include special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits). In addition to hardware, the devices may include code that creates an execution environment for the computer program in discussion, such as code constituting processor firmware, protocol stacks, database management systems, operating systems, cross-platform runtime environments, virtual machines, or combinations of one or more of these. The devices and execution environments can implement various computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.

[0121] Computer programs (also referred to as programs, software, software applications, scripts, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as standalone programs or as modules, components, subroutines, objects, or other units suitable for use in a computing environment. A computer program may, but does not necessarily, 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), a single file dedicated to the program in question, or multiple coordinated files (e.g., files storing one or more modules, subroutines, or code sections). A computer program can be deployed to execute on one computer or multiple computers located in one location or distributed across multiple locations and interconnected via a communication network.

[0122] The processes and logic flows described in this specification can be executed by one or more programmable processors that execute one or more computer programs to perform actions by manipulating input data and generating outputs. The processes and logic flows can also be executed by, and the apparatus can be implemented as, special-purpose logic circuits, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits).

[0123] Processors suitable for executing computer programs include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The basic components of a computer are a processor for performing actions according to instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) or be operatively coupled to receive data from or transfer data to one or more mass storage devices, or both, said mass storage devices for storing data. However, a computer need not have such devices. Furthermore, a computer can be embedded in another device, such as, to name a few, 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). Suitable devices for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices like EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by dedicated logic circuitry or incorporated into dedicated logic circuitry.

[0124] To provide interaction with a user, embodiments of the subject matter described herein can be implemented on a computer having: a display device for displaying information to a user, such as an LCD (liquid crystal display), an LED (light-emitting diode) display, or an OLED (organic light-emitting diode) display; and a keyboard and pointing device, such as a mouse or trackball, that the user can use to provide input to the computer. In some implementations, a touchscreen can be used to display information and receive input from the user. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be in any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, voice, or tactile input. Additionally, the computer can interact with the user by sending and receiving documents to and from the device used by the user; for example, sending a webpage to a web browser on the user's client device in response to a request received from a web browser.

[0125] Embodiments of the subject matter described herein can be implemented in computing systems that include backend components (e.g., as data servers); or middleware components (e.g., application servers); or frontend components (e.g., client computers) 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 backend, middleware, or frontend components. Components of the system can be interconnected via any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks (“LANs”) and wide area networks (“WANs”), interconnected networks (e.g., the Internet), and peer-to-peer networks (e.g., self-organizing peer-to-peer networks).

[0126] A computing system may include any number of clients and servers. Clients and servers are typically located far apart and interact via a communication network. The client-server relationship is established by computer programs running on respective computers that have a client-server relationship with each other. In some embodiments, the server transmits data (e.g., HTML pages) to the client device (e.g., to display data to a user interacting with the client device and to receive user input from that user). Data generated at the client device (e.g., the result of user interaction) may be received at the server from the client device.

[0127] In some embodiments, the signal analyzer accepts an acoustic dataset recorded from the test material as input. In some embodiments, the acoustic dataset represents at least a portion of the sweep frequency detected after the sweep encounters the material of interest. In some embodiments, the detected portion of the sweep frequency constitutes the sound waves reflected by the material of interest. In other embodiments, the detected portion of the sweep frequency constitutes the sound waves that have passed through the material of interest. Alternatively, the acoustic dataset represents a burst of acoustic energy at a single frequency that has been reflected or passed through the material of interest.

[0128] Figure 1 An embodiment of a system 100 useful for tissue processing (e.g., for optimized tissue fixation, dehydration, removal, or embedding) according to exemplary embodiments of the present disclosure is shown. System 100 includes an acoustic monitoring device 102 communicatively coupled to a memory 110 for storing a plurality of processing modules or logic instructions executed by a processor 105 coupled to a computer 101. The acoustic monitoring device 102 may include the previously mentioned acoustic detectors, which include one or more transmitters and one or more receivers. In some embodiments, the tissue sample may be immersed in a liquid fixative, while the transmitters and receivers communicate to detect the time-of-flight (TOF) of sound waves.

[0129] In some embodiments, the system 100 employs one or more processors 105 and at least one memory 110, the at least one memory 110 storing non-transitory computer-readable instructions for execution by the one or more processors to cause the one or more processors to execute instructions (or stored data) in one or more modules, the modules including: a tissue analysis module 111 for receiving information related to tissue blocks via user input or electronic input and for determining tissue characteristics, such as the velocity of sound in the tissue; a TOF modeling module 112 for simulating the spatial correlation of relative fixative or reagent concentrations for various times and model diffusion constants to generate a time-varying (“expected” or “modeled”) TOF signal and outputting a model decay constant; and a TOF measurement module 113 for determining the actual TOF signal of the tissue, calculating the spatial average, and generating an experimental decay constant, the experimental decay constant depending on tissue characteristics (e.g., actual cell size). The system includes inputs from acoustic monitoring device 102, cell type, cell density, cell size, and the effects of sample preparation and / or sample staining; and correlation module 114, which correlates (e.g., compares) experimental and modeling TOF data, determines the diffusivity constant of the tissue sample based on the minimum of the correlation error function, generates a second model TOF signal using the diffusivity constant determined in modeling module 112 along with candidate porosity values ​​for the tissue sample, and again uses correlation module 114 to perform a second correlation between the second model TOF signal based on the determined diffusivity constant and candidate porosity of the sample and the experimental TOF data, determines the porosity of the tissue sample based on the minimum of the error function of the second correlation between the experimental TOF data and the model TOF signal generated using the determined diffusivity constant, and determines the concentration of the reagent in the sample at a specific spatial and temporal point based on the experimental TOF signal, the determined diffusivity constant, and the determined porosity. These and other operations performed by these modules can result in the output of quantitative or graphical results to the user or computer 101. Therefore, although not in Figure 1 As shown, however, computer 101 may also include user input and output devices such as a keyboard, mouse, stylus, and monitor / touchscreen.

[0130] As described above, the module includes logic executed by processor 105. As used herein and throughout this disclosure, "logic" means any information that can be applied to affect processor operation and has the form of instruction signals and / or data. Software is an example of such logic. Examples of processors are computer processors (processing units), microprocessors, digital signal processors, controllers, and microcontrollers, etc. Logic can be formed by signals stored on a computer-readable medium (such as memory 110), which in exemplary embodiments may be random access memory (RAM), read-only memory (ROM), erasable / electrically erasable programmable read-only memory (EPROM / EEPROM), flash memory, etc. Logic can also include digital and / or analog hardware circuitry, such as hardware circuitry including AND, OR, XOR, NAND, NOR, and other logical operations. Logic can be formed by a combination of software and hardware. On a network, logic can be programmed on a server or a complex of servers. A particular logical unit is not limited to a single logical location on the network. Furthermore, modules do not necessarily execute in any particular order. Each module can call another module when needed.

[0131] In some embodiments, the acoustic monitoring device 102 can be adapted to a commercial "immersion" tissue processor, such as Electron Microscopy Sciences (RTM)'s Lynx II. A mechanical head designed using Solidworks® software can fit around and seal a standard reagent cartridge. Once sealed, an external vacuum system can be activated to degas the bulky reagents and the contents of the cartridge, including the tissue. A cartridge holder designed for use with standard-sized histology cartridges (such as CellPath (RTM)'s CellSafe 5) or biopsy capsules for smaller tissue samples (such as CellPath (RTM)'s CellSafe biopsy capsules) can be utilized. Each holder will securely hold the tissue to prevent the sample from slipping during the experiment. The cartridge holder can be attached to a vertical translation arm that will slide the cartridge holder in one direction. The mechanical head can be designed with two metal supports on either side of the tissue cartridge, one support housing five transmission transducers and the other housing five receiving transducers spatially aligned with their corresponding transmission transducers. The receiving support can also house a pair of transducers oriented orthogonal to the propagation axes of the other transducers. After each acquisition, the orthogonal sensors can calculate reference TOF values ​​to detect spatiotemporal variations in the fluid that have a profound impact on the speed of sound. Additionally, at the end of each 2D acquisition, the cartridge can be raised and a second reference acquisition can be performed. These reference TOF values ​​can be used to compensate for environmental fluctuations caused by formalin. Environmental fluctuations caused by formalin or any other fixative can include, for example, porous materials, vibration, and temperature fluctuations within the container.

[0132] Figure 2A and 2B Examples of ultrasound scan patterns from biopsy capsules and standard-sized cartridges are depicted, respectively. The measurement and modeling procedures described herein for tissue samples can be applied similarly to other forms of porous materials. Therefore, while this disclosure illustrates modeling in the context of tissue samples, such examples are not limiting, and the techniques can be applied to other materials, such as any porous material.

[0133] As described herein, measurements from acoustic sensors in an acoustic monitoring device can be used to track changes and / or rates of change in the time-of-flight (TOF) of acoustic signals passing through tissue samples. This includes monitoring tissue samples at different locations over time to determine the diffusion or diffusion rate over time.

[0134] For example, a “different location,” also known as a “candidate diffusion rate location,” can be a location within or on the surface of a tissue sample. According to some embodiments, the sample can be positioned at different “sample locations” by relative movement of the biopsy capsule and the acoustic beam path. This relative movement can include moving the receiver and / or transducer in a stepwise or continuous manner to “scan” the sample. Alternatively, the cartridge can be repositioned using a movable cartridge holder.

[0135] For example, to image all the tissue in the cartridge, the cartridge holder can be sequentially raised vertically by ≈1 mm, and TOF values ​​can be acquired at each new location, such as... Figure 2A and 2B The process described herein can be repeated to cover the entire open opening of the card holder. (Reference) Figure 2A When imaging the tissue in the biopsy capsule 220, the signal is calculated based on all five transducer pairs, resulting in... Figure 2A The scanned pattern depicted in the image. Alternatively, when... Figure 2B When imaging tissue in the standard-sized cartridge 221 depicted, the second and fourth transducer pairs can be turned off, and TOF values ​​are acquired between the first, third, and fifth transducer pairs located at the respective centers of the three intermediate subdivisions of the standard-sized cartridge 221. Two tissue cores can then be placed in each column, one at the top and one at the bottom, thereby enabling simultaneous acquisition of TOF traces from six samples (2 rows x 3 columns) and achieving significantly reduced variability with operation and increased throughput. In this exemplary embodiment, the half-width at half-maximum (WHM) of the ultrasound beam is approximately 2.2 mm.

[0136] Acoustic sensors in acoustic monitoring equipment may include a pair of 4MHz focused transducers, such as CNIRHurricane Tech (Shenzhen) Co., Ltd. (RTM) TA0040104-10, which are spatially aligned with a tissue sample placed at their common focal point. One transducer, designated as the transmitter, can emit acoustic pulses that pass through the coupling fluid (i.e., formalin) and the tissue and are detected by the receiving transducer.

[0137] Figure 2CA timing diagram illustrating an exemplary embodiment of this disclosure is shown. Initially, the transmitting transducer may be programmed with a waveform generator (such as Analog Devices (RTM)'s AD5930) to transmit a sine wave over hundreds of microseconds. This pulse sequence, after passing through fluid and tissue, can then be detected by the receiving transducer. The received ultrasonic sine wave and the transmitted sine wave can be compared using, for example, a digital phase comparator (such as Analog Devices' AD8302). During the time overlap between the transmitted and received pulses, the output of the phase comparator produces a valid reading. The output of the phase comparator is allowed to stabilize before being queried using an analog-to-digital converter integrated on a microcontroller (such as Atmel (RTM)'s ATmega2560). This process can then be repeated across the transducer's bandwidth at multiple acoustic frequencies to establish a phase relationship between the input and output sine waves across a frequency range. The acoustic phase-sweep frequency is directly used to calculate the time of travel (TOF) using a post-processing algorithm similar to acoustic interferometry, and can detect the travel time with sub-nanosecond accuracy.

[0138] In some embodiments, the “measured TOF” (i.e., the “measured TOF value”) obtained for a specific time point and a specific candidate spread rate point is calculated based on the phase shift measured between the transmitted ultrasound signal and the corresponding, received ultrasound signal, whereby the beam path of the ultrasound signal passes through the specific candidate spread rate point, and thus the phase shift is measured at the specific time point.

[0139] Figure 3 A method for obtaining a diffusivity coefficient for a tissue sample according to an exemplary embodiment of the present disclosure is shown. The operation disclosed in this embodiment can be performed by any electronic or computer-based system, including… Figure 1 The system performs the operation. In some embodiments, the operation may be encoded on a computer-readable medium (such as memory) and executed by a processor, resulting in output that may be presented to a human operator or used in subsequent operations. Furthermore, the operation may be performed in any order other than that disclosed herein, provided that the spirit of this disclosure is maintained.

[0140] In some embodiments, the method may include calculating the velocity of sound in a tissue sample (S330). This operation includes calculating the velocity of sound in a reagent in which the tissue sample is immersed. For example, the distance between ultrasonic transducers. d sensor (i.e., the distance between the transmitting and receiving transducers) can be accurately measured, and the travel time between the ultrasonic transmitter and receiver in pure reagents is also measured. t reagent The measured velocity of sound in the reagentr reagent It is calculated as follows:

[0141]

[0142] In some embodiments, tissue thickness can also be obtained via measurement or user input. Various suitable techniques can be used to obtain tissue thickness, including ultrasound, mechanical, and optical methods. Finally, the velocity of sound is determined (S330) by obtaining the phase delay relative to the bulk reagent (e.g., the fixation solution) from the undiffused tissue (i.e., a tissue sample where the fixation solution has not yet been applied):

[0143] as well as

[0144]

[0145] In some embodiments, a specific equation is derived based on the known geometry of the tissue sample, and typically, this equation represents the velocity of sound at time t=0 in a non-diffused tissue sample (i.e., a tissue sample without reagents, such as a fixative solution). In experimental embodiments, for example, the velocity of sound in a tissue sample can be calculated as follows: first, based on the distance between two ultrasonic transducers (which are also referred to herein as “sensors”). d sensor To calculate the speed of sound in the reagent, the distance ( d sensor For example, the sensor spacing is accurately measured using calipers. In this example, the sensor spacing is measured using calipers, and the sensor spacing... d sensor = 22.4 mm. Next, the travel time required for the acoustic pulse to pass through the reagent (no tissue) between the sensors ( t reagent This can be accurately recorded using applicable procedures. In the experimental example, for bulk reagents of 10% NBF (neutral buffered formalin), t reagent =16.71 μs The speed of sound in the reagent ( r reagent Then it can be calculated as:

[0146]

[0147] In this specific experiment, a 6mm histological biopsy core was used to core the tonsil sample fragments to ensure accurate and standardized sample thickness. d tissue =6mm), and in the presence of tissue ( ttissue+reagent ) and in the absence of an organization ( t reagent Calculate the TOF difference (Δt) between acoustic sensors:

[0148]

[0149] In some embodiments, time t reagent This is the time required for the ultrasound signal to travel the distance from the transmitting transducer to the receiving transducer, thus passing through the reagent volume but not through the tissue sample. This transit time can be measured, for example, by placing a biopsy capsule with the same diameter as the tissue (e.g., 6 mm) between the two sensors and performing a TOF measurement on the signal that has only passed through the reagent and not the tissue.

[0150] In some embodiments, time t tissue This is the time required for the ultrasound signal to travel the distance from the transmitting transducer to the receiving transducer, thereby passing through a tissue sample that does not contain reagents and is not surrounded by reagents. In some embodiments, the transit time can be measured, for example, by placing a biopsy capsule between the two sensors before adding reagents to the capsule and performing a TOF measurement for the signal that has only passed through the tissue.

[0151] In some embodiments, the time difference (or "TOF difference") Δt caused by the tissue, its thickness, and the velocity of sound in the reagent can be used to calculate the velocity of sound in the undiffused tissue using the following equation (t tissue (t=0)), the equation is derived based on the known geometry of the sample (e.g., cylindrical, cubic, box-shaped, etc.):

[0152]

[0153] Subsequently, a modeling process is performed to model the TOF over a variety of candidate diffusion constants. In some embodiments, the candidate diffusion constants comprise a series of constants selected from known or prior knowledge of tissue properties obtained from literature (S331). In some embodiments, the candidate diffusion constants are not precise, but are simply based on a rough estimate of what range is possible for a particular tissue or material under observation. In some embodiments, the estimated candidate diffusion constants are provided to the modeling process (steps S332-S335), where the minimum of an error function is determined (S337) to obtain the true diffusion constant of the tissue. In other words, the method tracks the difference between experimentally measured TOF diffusion curves and a series of modeled diffusion curves with different diffusion constants.

[0154] For example, when choosing one of several candidate diffusion constants, it is based on the reagent concentration C as a function of time and space. reagen The calculations use the solution to the thermal equation for cylindrical objects to simulate the spatial correlation of reagent concentrations in tissue samples (S332):

[0155]

[0156] Where x is the spatial coordinate along the depth direction of the tissue, R0 is the sample radius, D is the candidate diffusion rate constant, t is time, J0 is the first and 0th order Bessel function, J1 is the first and 1st order Bessel function, and α n It is the position of the nth root of the 0th order Bessel function, and c max This is the maximum concentration of the reagent. In other words, the summation of the coefficients of each of these Bessel functions (higher-order differential equations) provides the constant, i.e., the diffusivity constant, based on space, time, and rate. Although the equation is specific to the cylindrical tissue samples disclosed in these experimental embodiments, and the equation will vary depending on the shape or boundary conditions, the solution to the thermal equation for any shape can provide the diffusivity constant for that shape. For example, the thermal equation for objects having spherical, cubic, or rectangular block shapes can also be used in the disclosed method.

[0157] In some embodiments, this step is repeated for multiple time points (S333-S334) to obtain a time-varying TOF (corresponding to the expected reagent concentration, since the integral of the expected reagent concentration at a specific time point can be used to calculate the sound velocity difference) (S335). For example, determining whether the diffusion time has ended. In some embodiments, this diffusion time may be based on the type of system or hardware used. For each time interval T, steps S333, S334, and S332 are repeated until the modeling time ends, after which the modeled reagent concentration is converted into a time-varying TOF signal (S335).

[0158] In the experimental embodiment, the candidate diffusion constant D was used. candidate Each of these values ​​is included in the following range of values:

[0159]

[0160] In some embodiments, the tissue sample is cored using a cylindrical biopsy core punch, and thus can be well approximated by a cylinder. In some embodiments, the solution to the above thermal equation is then used to calculate the expected concentration (c) of the reagent in the tissue sample. reagent ), and for the first time point in the experiment, i.e., after 104 seconds of diffusion (based on the time interval between TOF acquisitions used in the system performing the disclosed experiment), in Figure 5AThe solution depicts the reagent concentration along the depth direction of the tissue. For example, a particular system can regularly measure new TOF values ​​for each of a number of different spatial locations, also referred to here as "pixels". Each "pixel" can therefore have an update rate that assigns a new TOF value, for example, every 10⁴ seconds.

[0161] Figure 5A The simulated concentration gradient of 10% NBF into a 6 mm tissue sample after 104 seconds of passive diffusion is shown, as calculated according to the thermal equation in the experimental embodiment. Furthermore, these steps were repeated every 104 seconds throughout the experimental period (8.5 hours in the experimental embodiment) to repeatedly determine the reagent concentration throughout the tissue, and the results are presented in… Figure 5B It is depicted in the middle.

[0162] Figure 5B c is shown reagent A plot of (t,r) showing the ("expected", "modeled", or "thermal equation-based") concentration of the reagent at all locations in the tissue (horizontal axis) and at all times (curve moving upwards).

[0163] Reference Back Figure 3 The results of the reagent modeling steps (S332-S334) can be used to predict the contribution to the ultrasound signal based on the fact that the ultrasound detection mechanism linearly accumulates phase delay across the entire depth of the tissue.

[0164] Since ultrasound detects the integrated signal from all tissues in the depth direction (i.e., along the propagation axis of the US beam), and is therefore sensitive to the integrated amount of fluid exchange in the depth direction, the "integral-expected" reagent concentration c can be calculated. detected This is also referred to as the "detected reagent concentration." In some embodiments, the "detected reagent concentration" is not a value obtained through empirical testing. Instead, it is a derivative value, created by spatially integrating all expected reagent concentrations calculated for a specific time point t and for a specific candidate diffusion constant. In some embodiments, the spatial integral may cover, for example, the radius of a tissue sample.

[0165] The concentration c of the reagent being tested can be calculated using the following equation. detected :

[0166]

[0167] In some embodiments, the integrated reagent concentration c detectedUsed to calculate the total amount of reagent at a specific time point. For example, additional volume and / or weight information of the sample can be used to calculate the absolute amount of reagent. Alternatively, and in some embodiments, the amount of reagent is calculated in relative units, such as as a percentage value [%] indicating, for example, the volume fraction of the sample that has been diffused by the reagent.

[0168] After simulating (i.e. calculating based on the thermal equation model) the detected reagent concentration for a given candidate diffusion rate constant and a given time point, the detected concentration can then be converted into a TOF signal (S335) as a linear combination of undiffused tissue and reagent using the following equation:

[0169]

[0170] Where r tissue (t=0) is the velocity of sound in the undiffused tissue, and ρ is the volumetric porosity of the tissue, representing the volume fraction of the tissue sample capable of fluid exchange with bulk reagent. The equation thus models the change in the TOF signal from diffusion as a linear combination of two distinct velocities (tissue and reagent). Since the TOF of the corresponding velocities of sound for pure tissue on one hand and pure reagent on the other can be readily determined empirically (e.g., by TOF measurements based on the corresponding phase shifts), the amount of reagent that has diffused into the sample at a given time point can be readily determined.

[0171] In some embodiments, the TOF contribution of a pure tissue sample (TOF contribution without bulk fluid (such as sample buffer or tissue fluid)) can be obtained by subtracting the TOF contribution measured for a tissue sample including and / or surrounded by bulk fluid from the TOF contribution measured for an ultrasound signal that has traversed the corresponding inter-transducer distance filled only with bulk fluid.

[0172] Figure 6A and 6B Plots depicting simulated, "detected," or "integrated" NBF concentrations via ultrasound throughout the experiment are presented. Figure 6A ), and a plot of the simulated (or "expected") TOF signal for the first candidate diffusion rate constant ( Figure 6B , where D = 0.01 μm² / ms). Figure 6B The TOF signal in the solution is calculated as the derivative of the corresponding integral concentration of the reagent.

[0173] At this point, the method generally correlates the modeled (or “simulated” or “expected”) Time of Flight (TOF) with the experimental TOF (S336), which is determined by measuring different regions of interest (ROIs) (also referred to as “candidate diffusion rate points”) within the tissue sample and determining the minimum of the error function to obtain the true diffusion rate constant. In this example, for each modeled TOF selected within the range specified by (S322), the experimental TOF is correlated (S336), and it is determined whether the error is minimized (S337). In some embodiments, if the error is not minimized, the next diffusion constant is selected (S338), and the modeling process is repeated for the new diffusion constant (S332-S335). In some embodiments, if it is determined that the error is minimized based on the correlation (S336) (S337), then the true diffusion rate constant is determined (S339), and the method ends.

[0174] Figure 4 The replacement method is illustrated, wherein all candidate diffusion rate constants are first used to perform modeling based on steps S446-S447, and correlation is performed after all diffusion rate constants have been processed (S448). Figure 7 The diagram shows a depiction of the time-varying TOF signal calculated for all potential diffusion rate constants. For example, Figure 7 Simulated TOF traces were plotted for 6 mm tissue samples over an 8.5-hour experiment at constant diffusion rates ranging from 0.01 to 2.0 μm² / ms. Figure 4 In the embodiment, error minimization is performed within the true diffusion rate constant determination step S439.

[0175] In either case, experimental TOF must be determined for correlation to occur. In some embodiments, experimental TOF can be determined by measuring different spatial regions of interest (ROIs) within the tissue. In some embodiments, each signal has a contribution from background reagents, which is subtracted to isolate the contribution from active diffusion into the tissue. In some embodiments, individual TOF trends are smoothed temporally via filtering. In some embodiments, these spatially distinct TOF trends are then spatially averaged to determine the average rate of diffusion into the tissue at 10% NBF.

[0176] Figure 8A and 8B The experimentally calculated TOF trends collected from 6 mm fragments of human tonsil samples were depicted. Figure 8A ) and spatially averaged TOF signals ( Figure 8B ), which represents the average rate and amount of fluid exchanged from 10% NBF into the tissue.

[0177] In some embodiments, the average rate of diffusion into the tissue is correlated with a single exponential signal (which is transmitted via...) Figure 8B The dashed lines in the diagram are highly correlated and obtained through the following equation:

[0178]

[0179] Where A is the amplitude of the Time of Flight (TOF) in nanoseconds (i.e., the TOF difference between undiffused and fully diffused tissue samples), and τ experimental It is the sample decay constant, representing the time required for the TOF to decay to 37% of its amplitude or equivalently by 63%, and the offset is the vertical offset of the decay function given above.

[0180] 63% can be obtained through the following calculation: over time Place, .

[0181] Therefore, it is assumed that TOF decreases with increasing reagent concentration in the sample; however, the method can also be applied to reagents that increase the measured TOF upon diffusion into the sample. In a 6 mm segment of a human tonsil in the experimental example, Hours. Therefore, based on multiple TOF values ​​already determined experimentally for multiple successive time points, the decay constant of the tissue sample can be calculated, for example, by plotting the amplitude of the TOF signal over time using the Tunneling time plot, analyzing the plot to identify the offset, and solving for the aforementioned solution of the decay constant.

[0182] In some embodiments, error is associated ( Figure 3 S336 in Figure 4 S448 in the process is performed to determine the error of the modeled (“expected”) TOF relative to the experimental TOF. With the simulated and experimental TOF signals already computed, the difference between the two signals can be calculated to see if the candidate diffusion rate constant minimizes the difference between the two signals (S337).

[0183] In some embodiments, the error function can be calculated in several different ways, for example using one of the following equations:

[0184]

[0185] In some embodiments, the first error function calculates the point-by-point difference between the simulated (“modeled”, “expected”) and experimentally measured TOF signals.

[0186] In some embodiments, the second error function specifically compares the diffusion rates between simulated and modeled TOF signals by calculating the sum of the squares of the differences between each attenuation constant. Experimental attenuation constant τ experimental It can be obtained experimentally as described above. Similarly, the "modeled," "expected," or "simulated" decay constant τ can be obtained from the modeled ("expected") TOF signal at successive time points that also follow the decay function. simulated .

[0187] Based on the output of the error function, the true diffusivity constant (S339) can be determined. The true diffusivity constant is calculated as the minimum value of the error function, for example:

[0188]

[0189] This equation enables the determination of candidate diffusion coefficients, which produces a TOF signal that is as close as possible to the experimental data.

[0190] For example, regarding Figure 3 The method described herein can determine an error function for each candidate diffusion rate constant until the error is minimized (S337). Alternatively, in Figure 4 In the method, after all candidate diffusion rate constants have been processed, an association with the experimental Time of Flight (TOF) can be performed, at which point the determination of the true diffusion rate constant (S439) includes determining the minimum of the error function. In some embodiments, the minimum of the error function is ideally zero, or as close to zero as possible. In some embodiments, any error function known in the art can be used with the objective of minimizing the error between the modeled coefficients and the experimental coefficients, as disclosed herein.

[0191] Figure 9A and 9B Plots of the calculated error function as a function of the candidate diffusion rate constant between the simulated TOF signal and the experimentally measured TOF signal are shown separately. Figure 9A , ) and a magnified view of the error function ( Figure 9B In the experimental embodiment, the minimum value of the error function was calculated at D = 0.1618 μm. 2 / ms. The validity of the reconstructed constants was tested and used to inversely simulate the TOF trend. Figure 10 The TOF trend was depicted, calculated using this diffusion rate constant and plotted along experimental TOF measured in a 6 mm segment of the human tonsil. Figure 10 The figure shows the experimentally calculated TOF trend (dashed line) of a 6mm human tonsil fragment from 10% NBF, as well as the trend for D...reconstructed = 0.168 μm 2 The TOF trend (solid line) is modeled at / ms. In this embodiment, τ experimental =2.830 hours, and τ simulated =2.829 hours.

[0192] Furthermore, the same process was repeated for several samples of 6 mm human tonsils, using the diffusion constant successfully reconstructed for all samples, such as Figure 11A and 11B As depicted in the text. Figure 11A The reconstructed diffusion constants for 23 samples of 6 mm human tonsils are shown. Line 1151 represents the mean. Figure 11B The diagram shows a box plot illustrating the distribution of the reconstructed diffusivity constant. Line 1152 represents the median, and box 1153 extends from 25% to 75%, while whisker 1154 extends from 5% to 95%. Overall, the algorithm predicts a mean diffusivity constant of 0.1849 μm² / ms for 6 mm tonsil samples, with a relatively tight distribution and a standard deviation of 0.0545 μm² / ms.

[0193] Figure 12A A system for monitoring the time of flight of an ultrasonic signal according to an embodiment of this disclosure is illustrated. The ultrasonic-based time-of-flight (TOF) monitoring system may include one or more pairs of transducers (e.g., CNIRHurricaneTech TA0040104-10) for performing time-of-flight measurements based on the phase shift of the ultrasonic signal. Figure 12AIn the embodiment depicted, the system includes at least one pair of transducers comprising an ultrasound (“US”) transmitter 902 and an ultrasound receiver 904, spatially aligned such that a tissue sample 910 placed in a beam path 914 from the transmitter to the receiver is located near the common focal point of the two transducers 902, 904. The tissue sample 910 may be contained in, for example, a sample container 912 (e.g., a standard histology cartridge (such as CellPath’s “CellSafe 5”) or a biopsy capsule (such as CellPath’s “CellSafe Biopsy Capsule”)) filled with a fixative solution. Phase-shift-based TOF measurements are performed before and after the biopsy capsule 912 is filled with the fixative solution, and as the solution slowly diffuses into the sample. The transducer acting as the transmitter emits an acoustic pulse that travels through the tissue and is detected by the other transducer acting as the receiver. The total distance between the two transducers constituting the transmitter-receiver transducer pair is referred to as “L”. The total time required for an ultrasonic signal to travel the distance between transmitter 902 and receiver 904 can be referred to as the signal's time of flight. Transmitter 902 can be focused, for example, at 4 MHz and supports a frequency sweep range of 3.7–4.3 MHz.

[0194] In some embodiments, the distance L is assumed to be known, or at least approximately known. For example, the distance to the transducer may be accurately measured (e.g., by optical, ultrasonic, or other measurement techniques) or may be disclosed by the manufacturer of the acoustic monitoring system.

[0195] In some embodiments, the transmitting transducer 902 is programmable with a waveform generator (e.g., AD5930 from Analog Devices) for transmitting a sine wave (or “sine signal”) at a defined frequency within defined time intervals (e.g., hundreds of microseconds). In some embodiments, the signal is detected by the receiving transducer 904 after passing through fluid and / or tissue. In some embodiments, the received ultrasound signal 922 and the emitted (also referred to as “transmitted”) sine signal 920 are electronically compared using a digital phase comparator (e.g., AD8302 from Analog Devices).

[0196] As used herein, the term "received" "signal" (or wave) refers to a signal whose properties (phase, amplitude, and / or frequency, etc.) are identified and provided by the transducer (e.g., receiver 904) that receives the signal. Thus, the properties of the signal are identified after the signal has passed through a sample or any other kind of material.

[0197] As used herein, the terms “transmitted” or “emitted” “signal” (or wave) refer to a signal whose properties (phase, amplitude, and / or frequency, etc.) are identified by the transducer (e.g., transmitter 902) that transmits the signal. In some embodiments, the signal properties are identified before the signal has passed through a sample or any other kind of material.

[0198] For example, the transmitted signal can be characterized by the signal properties identified by the transmitting transducer, and the received signal can be characterized by the signal properties measured by the receiving transducer. Thus, the transmitting and receiving transducers are operatively coupled to the phase comparator of the acoustic monitoring system.

[0199] Figure 12B The determination of the Time-of-Flight (TOF) for a pure reagent is described, from which the velocity of the sound wave passing through the beam path of the pure reagent without a sample can be inferred. In this embodiment, the one or more transducer pairs 902, 904 and the sample container 912 can be moved relative to each other. In some embodiments, the system includes a container holder capable of repositioning the container 912 such that the US beam passes through a region 914 of the container containing only the fixative solution and not tissue.

[0200] At time A, before the tissue has been immersed in the fixation solution, the measured phase shift... The Time-of-Flight (TOF) method, which obtains the acoustic signal across the distance between transducers, is used for... Figure 12A As described. In this case, the beam path passes through a reagent-free sample. Since L is known, the measured TOF can be used to calculate the velocity of the acoustic signal across that distance in the presence of an undiffracted sample.

[0201] At time B, when the tissue is immersed in the fixation solution, the measured phase shift... The time-of-flight (TOF) is obtained by measuring the distance the acoustic signal travels between the transducers. In this case, the beam path passes through a sample container containing only the reagent and no sample (or passes through the sample container at a location without a sample). Since L is known, the measured TOF can be used to calculate the speed at which the acoustic signal travels the distance in the presence of only the reagent (and the sample container), i.e., in the absence of a sample in the beam path.

[0202] In cases where another pair of transducers is configured to perform two measurements in parallel, time A and time B can represent the same point in time.

[0203] III. Example

[0204] An investigation was conducted into the disclosed method, which determines reagent concentrations across spatial and temporal dimensions within the sample and across tissue sample types. The sample was monitored using a Time-of-Flight (TOF) system as described above during cold immersion in NBF, and experimental TOF data extracted over time were obtained. Following TOF analysis, the sample was heated to fix the tissue and then processed in a tissue processor to prepare paraffin blocks. In some embodiments, the blocks were sectioned on a microtome and mounted on microscope slides, stained according to standard protocols, and in some instances read by a qualified slide reader to assess staining quality.

[0205] Figure 13 A model of reagent diffusion into a cylindrical object, such as a cylindrical tissue core, is shown. As can be seen, the reagent concentration initially increases rapidly at the edges of the tissue sample, and the concentration at the center initially increases slowly (if at all), thus causing the concentration change seen at the sample edges to lag, then accelerates at a later time point, and then begins to slow down again. In this model:

[0206]

[0207] Compared to Figure 5B Concentration changes over time are more variable in rate than changes seen for the percentage of diffusion. This is not unexpected, because percentage diffusion is an average measured across the entire sample, while concentration changes are location-specific.

[0208] Furthermore, since the porosity of the sample scales with A, therefore in the following equation:

[0209]

[0210] Once the diffusivity constant is known, candidate porosities can be used to calculate simulated TOF curves and compared with experimental TOF curves to generate an error, which can be minimized. The error function can be calculated in different ways, for example using one of the following:

[0211]

[0212] In some embodiments, the first error function calculates the point-by-point difference between the simulated (“modeled”, “expected”) TOF signal and the experimentally measured TOF signal.

[0213] In some embodiments, the second error function specifically compares the diffusion rates between the simulated and modeled TOF signals by calculating the difference in the sum of squares between each attenuation constant. Experimental attenuation constant τ experimentalIt can be obtained experimentally as described above. Similarly, the "modeled," "expected," or "simulated" decay constant τ can be obtained from the modeled ("expected") TOF signal at successive time points that also follow the decay function. simulated .

[0214] In some embodiments, the true porosity can be determined based on the output of an error function. The true porosity is calculated as the minimum value of the error function, for example:

[0215]

[0216] In some embodiments, once the porosity of the sample is determined, the reagent concentration at specific spatial and temporal points can be calculated using the following equation:

[0217]

[0218] Figure 14 The illustrations comparatively depict typical distributions of percentage diffusion of formalin solution into the center of tonsil tissue core samples (approximately 6 mm cylinders) at approximately 3 hours and 5 hours, where approximately 3-hour immersion produces fairly good staining, while approximately 5-hour immersion produces “ideal” staining. On average, samples subjected to approximately 3 hours of immersion will achieve approximately 52.6% percentage diffusion at the tissue center, and samples subjected to 5 hours of immersion will achieve an average percentage diffusion of approximately 76.9%. The approximately 95% prediction interval at approximately 5 hours indicates that the sample requires at least approximately 52.45% diffusion at the center to achieve “ideal” staining as judged by pathologist review.

[0219] Figure 15 Receiver operating characteristic (“ROC”) curves comparing staining quality (sensitivity vs. specificity) based on the percentage diffusion at the center of a tissue sample are shown. In this example, the area under the curve (AUC) of 0.8926 is generated using the percentage diffusion at the center of the tissue to predict staining quality based on the measurement of the percentage diffusion at the center of the tissue.

[0220] Figure 16 A typical graph is shown comparing the difference in the percentage of diffusion measured at the center of a tissue sample between 3 and 5 hours of exposure to the reagent, with the result being: the average difference in diffusion between 3 and 5 hours at the center of the tissue was 24.3%.

[0221] We now turn to the results obtained using the published method that determines the reagent concentration at a specific spatial point within a tissue sample based on TOF data. Figure 17 The raw data distribution of amygdala tissue volume porosity determined for several samples is shown. Figure 18 The porosity of the determined tonsillar tissue volume is shown. Figure 17 The corresponding boxes for the data must be distributed. As can be seen, the tonsil tissue, in particular, exhibits an average porosity of approximately 0.15.

[0222] Figure 19 The typical distribution of formaldehyde concentration at the center of a tonsil tissue core sample (approximately 6 mm cylinder) is shown at 3 and 5 hours, with the 3-hour immersion producing fairly good staining and the 5-hour immersion producing “ideal” staining. On average, samples subjected to 3 hours of immersion will reach a formaldehyde concentration of 92.3 mM at the tissue center, and samples subjected to 5 hours of immersion will reach an average concentration of 137.5 mM at the tissue center. The 95% prediction interval at 5 hours indicates that the sample should have achieved at least 91.07 mM formalin at the tissue center during the fixation period to achieve “ideal” staining as judged by pathologist review.

[0223] Figure 20 ROC curves for staining quality (sensitivity and specificity) based on formaldehyde concentration at the center of the tissue sample are shown. In this case, the AUC is 0.9256, which illustrates the difference between using the percentage of diffusion at the tissue center as a predicted value for staining quality (AUC - 0.8926) (e.g.). Figure 15 (As shown in the figure) the superiority of using the formaldehyde concentration at the center of the tissue as a predictor of color quality.

[0224] Similarly, Figure 21 This demonstrates the superiority of reagent concentration at the tissue center as a predictor of staining quality. Therefore, Figure 21 A graph is provided showing the difference in formaldehyde concentration at the center of tissue samples between approximately 3 hours and approximately 5 hours of immersion in NBF solution. Overall, the average concentration difference observed is approximately 45 mM. This is compared to the difference in the percentage of diffusion (24%). Figure 16 The difference in concentration at the tissue center between approximately 3 hours and approximately 5 hours, at approximately 33% (45 mM / 137 mM x 100%), is more striking, reflecting a difference in reagent concentration that occurs later in the immersion and may affect the staining quality at the tissue center. Again, this illustrates the advantage of using a method that provides a location- and time-specific measure within the sample volume (concentration in this case) over an average measure across the entire sample volume (as in the case of a measure of only the percentage of diffusion).

[0225] Having established that the disclosed method can be used to determine the porosity of tonsillar tissue, porosity was measured for approximately 10 different tissue types (approximately 80 samples), and the results are shown in... Figure 22 middle, Figure 22 The distribution of raw porosity for several tissue types is shown. Figure 23 A set of frame distributions of porosity for several tissue types is shown. As can be seen, for most tissue types, the average porosity (the lines in the frame) is between approximately 0.1 and approximately 0.2, while skin has a much higher porosity of more than approximately 0.3.

[0226] For comparison, Figure 24 The distribution of the determined diffusivity constants for several tissue types is shown, and Figure 25 A set of whisker distributions for the diffusivity constant is shown for several tissue types. The diffusivity constant is more variable than the average porosity determined across several tissue types.

[0227] Figure 26 The distribution of the original percentage diffusion at the center of the tissue sample at approximately 3, 5, and 6 hours is shown for several tissue types. Figure 27 A set of frame distributions are shown for several tissue types at approximately 3, 5, and 6 hours at the center of the tissue sample. Figure 28 The distribution of initial formaldehyde concentrations at the center of tissue samples at approximately 3, 5, and 6 hours is shown for several tissue types. Figure 29 A set of whisker distributions of formaldehyde concentrations at the center of tissue samples at approximately 3, 5, and 6 hours are shown for several tissue types. Based on the raw data and whisker distributions measured as percentage diffusion, and compared with those based on reagent concentrations determined at the tissue center, it is evident that the data tend to cluster more tightly when using concentration.

[0228] Figure 30 The distribution of the original formaldehyde concentration at the center of tissue samples for several tissue types after the indicated immersion time is shown, and Figure 31 A set of frame distributions of formaldehyde concentrations at the center of tissue samples for several tissue types are shown after the indicated immersion times. These results confirm the association between tissue-center formaldehyde concentrations above approximately 90 mM (such as above 100 mM) and “ideal” staining, as earlier studies have shown that fixation for at least approximately 6 hours (approximately 5 hours for tonsils) in the cold step of a cold + hot fixation protocol ensures “ideal” staining. The results were further confirmed by microscopic analysis, where a qualified reader determined… Figure 32 The organization indicated in the document does indeed describe the "ideal (optimal)" coloring after the indicated time.

[0229] Figure 33The original distribution of formaldehyde concentration at the center of tissue samples across all tissue types is shown after 6 hours of immersion in 10% NBF. Figure 34 The distribution of formaldehyde concentration at the center of tissue samples for all tissues after 6 hours of immersion in 10% NBF is shown. A formaldehyde concentration level of 90 mM (or 100 mM) for achieving “ideal staining” was confirmed across all tissue types. The difference between calculating the formaldehyde concentration at the center of tissue based on TOF data and simply using a standard fixation time protocol is that while approximately 6 hours of immersion may not be sufficient to achieve ideal staining for samples larger than approximately 6 mm in diameter, sufficient time to achieve at least 90 mM (or 100 mM) of formaldehyde at the center of the tissue will ensure “ideal” staining of the sample. Conversely, smaller samples (e.g., core biopsies) that could potentially be overfixed using a standard fixation time of approximately 6 hours can be processed only until the concentration at the center of the tissue reaches at least approximately 90 mM, resulting in a shorter overall analysis time.

[0230] Figure 35 An embodiment of the disclosed method for obtaining the diffusivity coefficient, porosity, and formaldehyde concentration at the center of a tissue sample is shown. At S430, the sound velocity of the sample is measured, as previously described... Figure 3 As described in the context. And like... Figure 3 In this embodiment, operations and determinations S431, S432, S433, S434, S435, S436, S437, and S438 are performed to define a diffusion constant. Once the diffusion constant is determined at S439, a range of porosity for modeling the diffusion of the reagent solution into the sample is set at S440. This range can be set by default or entered by the user. For example, based on the above regarding... Figure 22The porosity values ​​discussed, determined experimentally, should fall within a range of 0.05 to 0.50 (or narrower) to cover most—if not all—tissue types. Narrower ranges can be set when tissue types have already been checked, and, for example, the user can input tissue types to provide the model with an appropriate range of values ​​for exploration. At S441, S442, and S443, the diffusivity constant from S439 and the candidate porosity set at S440 are used to model the spatial correlation of the reagent over a series of time points T to T+n. The model constructed at this series of time points is then used to generate the expected TOF curve at S444 and correlated with the experimental TOF curve at S445. At S446, the error between the expected and experimental TOF curves is checked to see if it is at a minimum, and if so, the candidate tissue porosity is determined as the actual tissue porosity at S448. If not, the process is repeated at S447 with a second candidate porosity. Once both the diffusivity constant (S439) and porosity (S448) are defined, a spatial model of reagent concentration over time can be generated at S449. Once the spatial model of reagent concentration over time is established, the concentration at a specific point within the sample (such as at the center of the sample) at a specific time can be extracted from the model.

[0231] In the second set of experiments, the applicability of TOF measurements for monitoring the tissue sample preparation workflow after sample fixation was examined. Tissue samples were obtained as delabeled material. Fresh samples were collected from surgical excisions and cadaveric materials when fresh surgical material was unavailable. All samples were cored using a 6mm punch, or all samples were cut to a maximum thickness of approximately 6mm. In practice, due to the gel-like nature of tissues, samples 4–7mm thick were included in the study, although most samples (85%) were estimated to be 6mm thick. In total, a total of 250 tissues were collected from 8 different organs, including normal and cancerous breasts, normal and cancerous colons, normal and cancerous kidneys, normal and cancerous lungs, liver, fat, skin, and tonsils. Not all tissues were monitored with every reagent, as the study began with NBF, followed by monitoring with 3x ethanol dehydration, and finally monitoring with xylene removal. Data could not be collected if tissue slippage, reagent erroneous evaporation, or errors in the TOF instrument prevented successful monitoring of all tissues with all reagents. Overall, 170, 113, 123, 98, and 31 tissue samples were monitored in NBF, 70% ethanol, 90% ethanol, 100% ethanol, and xylene, respectively.

[0232] As previously described, acoustic monitoring technology was used to modify a commercial immersion tissue processor (Lynx II from Electron Microscopy Sciences). A custom-developed digital acoustic interferometry algorithm, described elsewhere, was used to detect small acoustic phase delays resulting from fluid exchange within the tissue with sub-nanosecond accuracy (25). A pair of 4 MHz focused transducers were spatially aligned, and tissue samples were placed near their common focus. The transmitting transducer was programmed to emit sinusoidal pulses that were detected by the receiving transducer after passing through the reagent and tissue, and the received pulses were used to calculate the passage time. The tissue was held in a grid biopsy cartridge (CellSafe 5 biopsy cartridge from CellPath USA) for measuring multiple samples at once, see [link to relevant documentation]. Figure 1 The cartridge holder is mechanically shifted so that diffusion throughout the tissue sample can be monitored with a spatial resolution of 1 mm. Baseline TOF values ​​are acquired by measuring only the TOF of the reagent, and this reference value is subtracted from the TOF in the presence of tissue to isolate phase delay from the tissue and compensate for environmental fluctuations in the reagent.

[0233] Figure 36AThe diagram shows a perspective view of an embodiment of a TOF-enabled tissue processing system 100, which includes an acoustic monitoring device 102 and several chambers 120, each chamber including a pool for holding processing chemicals. The acoustic monitoring device 102 can move between chambers 120 along with a sample to execute a tissue processing protocol. In other embodiments, the acoustic monitoring device may be integrated into the wall of a specific pool, and samples are moved into and out of the TOF pool and other pools according to the tissue processing protocol. In a particular embodiment, the TOF-enabled pool is a 70% EtOH pool, which advantageously allows for the automatic setting of subsequent processing steps based on the TOF characteristics of tissue in 70% ethanol. As used herein, "pool" refers to any enclosure in which tissue processing steps are performed. Those skilled in the art will recognize that many different tissue processor designs exist that can be modified or otherwise reengineered to include TOF measurement capabilities in one or more pools. It is also envisioned that data from a Time-of-Flight (TOF) enabled tissue processing system could be used to develop lookup tables for automated systems that allow users to program tissue processing protocols for specific types and sizes of tissues or for grouping specific types and sizes of tissues sharing similar processing requirements. The system could also provide users with guidance on similar tissue types that might be included in a given selected protocol. For example, a user could select a first sample of a specific tissue type and size, and then be presented with a list of other appropriate samples for batch processing along with the first sample. In this way, samples requiring only similar processing times can continue to be processed at the same pace, which can increase overall laboratory workflow efficiency.

[0234] Figure 36B A perspective view of an exemplary acoustic monitoring device 102 is shown. A tissue cartridge 150 is movably held within the monitoring device between an ultrasound transmitter 130 and an ultrasound receiver 140. Figure 36C A tissue processing cartridge 150 holding several tissue fragments 160 is shown. The relative movement between the ultrasound transmitter 130 and receiver 140 and the cartridge 150 makes it possible to measure the TOF in different parts of a single tissue sample, or to measure the TOF signal for different tissue fragments in the same cartridge.

[0235] For example, Figure 36DSeveral Time-of-Flight (TOF) traces obtained for a single tissue fragment immersed in 10% NBF are shown. These TOF traces were collected by moving the sample relative to the transmitter and receiver sections of an acoustic monitoring device. Individual TOF signals were filtered using a low-order median filter and a third-order Butterworth filter to reduce noise. To obtain a representative measure of the rate of fluid exchange, all individual TOF signals were averaged together to produce a single TOF curve representing the average diffusion rate of the entire sample. Figure 36E The spatially averaged TOF signal (solid line) and its exponential curve fit (dashed line) observed for a single tissue fragment are shown. Passive formalin diffusion into the tissue gradually increases the velocity of sound in the sample because the velocity of sound is faster in formalin compared to exchangeable fluids within the tissue. This increase in velocity results in a gradually decreasing acoustic travel time through the tissue. Consistent with expectations according to Fick's law, the sample initially experiences rapid exchange from a large concentration gradient and gradually tends towards no fluid exchange as diffusion equilibrium is achieved (see [link to Fick's Law]). Figure 36E This method also mitigates the effects of considerable spatial heterogeneity within the tissue. Finally, in practice, the TOF changes during cold formalin diffusion correlate well with single exponential decay, so the average diffusion curve was fitted to a single exponential function using nonlinear regression. Although the acoustic properties of tissue can change during tissue treatment for various reasons (such as tissue shrinkage, distortion, or becoming less compressible), it has previously been shown that the TOF signal is dominated—if not entirely—by fluid diffusion into and out of the tissue.

[0236] In some embodiments, a customized tissue treatment protocol is used to establish proof of the concept that our TOF monitoring technology can detect tissue alterations resulting from the diffusion of treatment reagents. Instead of subjecting tissue to a single reagent multiple times, the customized protocol according to this disclosure subjects the tissue to each reagent once for an extended period of time, enabling the detection of a continuous signal related to the diffusion rate of each particular reagent. TOF monitoring was performed sequentially for each tissue sample in cold 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and absolute xylene. The times and temperatures for all reagents in the customized TOF protocol are shown in Table 1 below. Tissues were fixed in formalin using two temperature-fixation methods, wherein the tissue was initially placed in cold NBF to allow unconstrained diffusion of formaldehyde before it was moved to heated NBF, rapidly initiating crosslinking. No TOF signal was reported in heated NBF because the tissue was already in diffusion equilibrium with the bulk reagent, and therefore no diffusion occurred. For practical reasons, TOF technology is not used to monitor paraffin embedding, such as wax-encased ultrasonic transducers, although in principle the method can detect the progress of paraffin embedding based on the differential sound velocities of xylene and paraffin.

[0237]

[0238] Table 1.

[0239] Table 1 illustrates the fixation and tissue processing steps for a custom TOF protocol used to study the diffusion rates of formalin, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and absolute xylene. s It is the speed of sound, and Δc s It is the difference in sound velocity between two subsequent reagents. Sound velocities referenced from various sources (25-27), approximately 70% and approximately 90% of the sound velocity were calculated as linear combinations of water and absolute ethanol.

[0240] The Time-of-Flight (TOF) system is used to study the rates of fluid exchange from multiple reagents to several different types of tissue using a customized tissue processing protocol. Example TOF curves from a small patch of kidney samples are shown for all five reagents monitored. Figure 37A , 37B Both the original (top screen plane) and spatially averaged (bottom screen plane) TOF signals are shown in 37C, 37D, and 37E. As the sample is moved to higher concentrations of ethanol, the TOF through the tissue continuously increases (i.e., decreases), as expected, since ultrasound travels slower in ethanol than in formalin. The reversal of the TOF polarity relative to NBF diffusion and the magnitude of the decrease in the ethanol signal are both consistent with the expected velocity of sound from these reagents. Finally, when the sample is moved from absolute ethanol to absolute xylene, the TOF decreases because the velocity of sound is greater in absolute xylene than in absolute ethanol. Consistent with our previous findings in NBF, the TOF-based diffusion signals from approximately 70%, 90%, 100%, and absolute xylene all correlate well with a single exponential function, as shown in... Figure 37A , 37B Visible in the bottom planes of 37C, 37D, and 37E. The mean deviation (S-error) of the fit to each index across all tissues and reagents is at most 3.22 ns (see [reference]). Figure 37F And as low as hundreds of picoseconds. The adjusted R² value of the TOF signal from all tissues and reagents is greater than 0.98 (see [link]). Figure 37G The signal-to-noise ratio (SNR) across all tissues and reagents is... Figure 37HThe results are shown below. Overall, the TOF signals from all tissues and reagents correlate well with a single exponential function, as the adjusted R² is typically greater than 0.98, and the average deviation from the fit is only 1–2% of the TOF amplitude, as indicated by the SNR for all reagents being between 50 and 100. It is noteworthy that the TOF measurements in approximately 70% ethanol have a high SNR, which advantageously aligns with the following: approximately 70% ethanol is the first reagent used in typical tissue processing protocols, and as shown below, the diffusion rate (and thus the end time) of subsequent tissue processing protocol steps can be predicted based on the TOF measured in 70% ethanol.

[0241] Figure 38A The absolute TOF signal over time for normal kidney tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ. Figure 38B The absolute TOF signal over time for normal breast tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ. Figure 38C The absolute TOF signals over time for normal colonic tissue are shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The black solid line represents the average signal, and the shaded area is ±σ. Figure 38D The absolute TOF signal over time for renal cell carcinoma tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ. Figure 38E The absolute TOF signal over time for breast cancer tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ. Figure 38F The absolute TOF signals over time for adipose tissue are shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0242] As expected based on the velocity of sound of these compounds, the TOF, which decreases in approximately 10% NBF, gradually increases with increasing ethanol concentration and eventually decreases in xylene. It should be noted that the amplitude of the three ethanol signals also decreases with increasing ethanol concentration, because replacing approximately 90% ethanol with approximately 100% ethanol produces a smaller velocity difference compared to exchanging approximately 70% and approximately 90% ethanol. Interestingly, fatty samples in xylene consistently show an increased TOF signal. Although not fully explained, this counterintuitive result may be a consequence of xylene removing a portion of the fatty sample, which has a faster velocity of sound than xylene, thus reducing the velocity of sound in the sample and increasing the observed TOF. Regardless of the exact mechanism, a reliable and stable TOF signal indicates when the sample and xylene are in equilibrium. TOF monitoring enables the quantitative tracking of the amount of fluid exchange that has occurred in a tissue sample and the rate at which it occurs.

[0243] Figure 39A The normalized TOF signal over time for normal kidney tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The black solid line represents the average signal, and the shaded area is ±σ. Figure 39B The normalized TOF signal over time for normal breast tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The black solid line represents the average signal, and the shaded area is ±σ. Figure 39C The normalized TOF signal over time for normal colonic tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The black solid line represents the average signal, and the shaded area is ±σ. Figure 39D The normalized TOF signal over time for renal cell carcinoma tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. Figure 39E The normalized TOF signal over time for breast cancer tissue is shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39F The normalized TOF signals over time for adipose tissue are shown in approximately 10% NBF, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and approximately 100% xylene. The solid black line represents the average signal, and the shaded area represents ±σ.

[0244] exist Figure 38A -F and Figure 39A The -F section shows the same example tissue type. The TOF signal is normalized to the average value for each chemical to help visualize the rate of fluid exchange and make it easier to see how long the process takes to complete. Visually, one can depict the differences in the rate at which samples diffuse chemicals; for example, cancerous kidney and breast samples have more variable processing rates compared to their normal counterparts, and in general, the diffusion rate of ethanol shows an increasing trend with concentration.

[0245] In summary, all data collected using the modified tissue processor, along with the distribution of TOF decay time and amplitude, were calculated. Figure 40A The distribution of all attenuation magnitudes by reagent group is shown. Solid lines represent the median, solid boxes represent 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles. 40B shows the distribution of attenuation magnitudes by reagent and tissue type. Negative magnitudes represent increasing TOF, and positive magnitudes represent decreasing TOF. Solid lines represent the median, solid boxes represent 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles (Ca = cancer). Since no acceptance criteria for the extent or percentage to which histological samples should be dehydrated in ethanol and removed in xylene have been previously provided, we have chosen to show here the time required for 90% diffusion of the sample. Figure 40C The distribution of time required for 90% diffusion by reagent group is shown. The solid line is the median, the solid box represents 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles. Figure 40D The distribution of time required for 90% diffusion of tissue by reagent and tissue type is shown. The solid line is the median, the solid box represents 25% to 75%, the whiskers extend to 1.5 times the interquartile range, and data outside the whiskers are indicated by circles. Ca = cancer. One can see that diffusion of approximately 70% ethanol is typically slower than diffusion of the other three reagents, although some samples, such as skin and fat, take relatively long to clear higher concentrations of ethanol and xylene. The rate of reagent clearance varies significantly across different organs. Note how slowly fat samples clear all three ethanol solutions, and the extreme variability in the case of skin samples.

[0246] The time for approximately 90% diffusion of a specific type of tissue in approximately 90% ethanol, 100% ethanol, and xylene was compared to the time required for a sample to become approximately 90% diffused in approximately 70% ethanol. Figure 41AThe diffusion times for approximately 90% ethanol versus approximately 70% ethanol are shown for several tissue types as indicated. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis, with specific tissue types labeled accordingly. Figure 41B The diffusion times for approximately 90% ethanol versus approximately 100% ethanol are shown for several tissue types as indicated. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis. Figure 41C The diffusion times of xylene for several tissue types, as labeled, are shown relative to the diffusion times of approximately 70% ethanol. Circles represent average diffusion times, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis. Tissues requiring longer processing times tend to also require longer times for all or most subsequent processing steps. Although not a perfect correlation, tissue types that tend to diffuse slowly with approximately 70% ethanol (such as fat and skin) and, to a lesser extent, breast tissue also tend to take longer to diffuse with approximately 90% ethanol, approximately 100% ethanol, and xylene. Using this perspective, a trend can be observed that indicates a correlation between the diffusion rate of subsequent reagents and the diffusion rate in approximately 70% ethanol.

[0247] A prediction criterion for optimal processing time was developed. Figure 42 , 44 The plots in Figures 46 and 46 show the mean and standard deviation of all the collected data, but only pairs of data points are included in the plots on each side. Figure 43A -D、 Figure 45A -D and Figure 47A The corresponding predictive statistical analysis described in -D shows that paired data indicate successful recording of diffusion times for both reagents studied. This is important because sample-to-sample variability, even within a single tissue type, can be significant and lead to misleading results. Figure 42 , 44 In 46, only paired data points were plotted for each of the three reagent pairs, and each pair of data points was empirically determined to be best correlated with a power function having varying constants. The curve fitting accuracy was extremely high for variations from the fitted at 20, 29, and 8 minutes, respectively, for approximately 90% ethanol, approximately 100% ethanol, and xylene. This indicates that by using curve fitting with the knowledge of the power function and the diffusion rate of 70% ethanol, one can accurately predict the diffusion rates of other chemicals. Furthermore, statistical analysis was performed on each experimental paired dataset, where prediction intervals were calculated around each best-fit line. The prediction intervals were... Figure 42 , 44All three of them are shown as dashed lines on the drawing (46). Figure 42 and 44 In this study, the best-fit line reaching the 99% confidence interval was approximately one hour, indicating that the rate of diffusion of approximately 70% ethanol from a sample could be used to predict how long it would take for approximately 99% of a tissue sample to diffuse into approximately 90% of 100% ethanol within one hour. For xylene (… Figure 46 The correlation is even stronger, allowing us to use the knowledge of the diffusion rate of approximately 70% ethanol to predict how long it will take for xylene to diffuse in a sample that will take less than 30 minutes. This robust correlation indicates that once the diffusion rate for 70% ethanol is determined, subsequent steps can be accurately predicted.

[0248] Figure 43A , 43B 43C and 43D show examples such as those targeting Figure 42 The regression shown is a statistical measure of the quality of fit. Figure 45A , 45B 45C and 45D show examples of what is intended for use with respect to... Figure 44 The regression shown is a measure of the statistical fit quality. Figure 47A , 47B 47C and 47D show examples of what is intended for use with respect to... Figure 46 The regression shown is a measure of the statistical fit quality.

[0249] In total, 126 samples from eight different organs, in addition to cancerous breast, colon, kidney, and lung, were monitored. Ethanol dehydration and xylene clearance were robustly characterized using acoustic TOF detection, and signal amplitude and fluid exchange rates were highly varied across samples and between different tissue types. The functional form of fluid exchange from all treatment agents was highly correlated with a single exponential (R²adj = 0.992 ± 0.02; bias of signal amplitude from fit = 1.5% ± 1). In some embodiments, the technique can be used to quantitatively assess the effects of treatment agents on overall tissue quality and downstream immune recognition of cancer biomarkers such as FoxP3. Furthermore, the disclosed method provides a way to accelerate tissue processing time and provides guidance for standardizing the processing of histological samples in a fully traceable and reproducible pre-analysis workflow. This disclosure illustrates real-time monitoring of the diffusion, dehydration, and clearance of formalin, graded ethanol, and xylene in in vitro tissue samples from many different tissue types using precise acoustic TOF detection. Using a modified tissue processor and a precise Time-of-Flight (TOF) calculation algorithm, the diffusion of non-nitrogenous fluid (NBF) in tissue is monitored in real time, with TOF diffusion trends from the treatment reagents consistent with previous results. This technique, combined with empirical measures of sample quality (e.g., pathologist scores), allows the relative amount of fluid exchange, exchange rate, and even concentration profiles within the tissue to be correlated with the time required for optimal processing of samples of various types, sizes, shapes, etc., and extends to tissue samples of other shapes using thermal equations for different sample shapes. For example, even complex sample shapes can be decomposed into sub-shapes based on 3D images, and calculations are performed for each sub-shape to determine the portion of the sample that will require the longest processing time in each reagent, thus setting the overall processing time for that particular sample. Similarly, the disclosed method implements a tissue processing system with compartments for separately and simultaneously processing batches of samples requiring similar processing times in different compartments. Real-time detection of fluid penetration using this capability enables, for the first time, the quantification of the extent to which samples are processed, facilitating the development of quantitative quality measures that can be used to study the potential impact of processing on histological tissues and downstream diagnostic testing. This capability enables faster processing of tissue samples because the precise time at which tissue diffusion ends can be determined. Even in batch processing scenarios, this technology can be used to develop faster, more consistent, and standardized processing protocols for clinical tissue samples, improving process reproducibility and quality assurance. Although not illustrated in the examples above, given the different sound velocities of xylene and paraffin, this technology can be extended to monitor paraffin embedding, which would enable the entire fixation and processing of histological samples that will be quantitatively tracked and studied.In some embodiments, the disclosed systems and methods can be used to optimize decalcification of bone segments, which can prove useful because poorly decalcified samples are unsuitable for IHC, and overexposure to decalcification reagents can impair tissue morphology, nuclear chromatin, and nucleic acids.

[0250] Furthermore, while all the tissue treatment TOF studies described have been discussed with reference to decay times and amplitudes, other measures of the extent to which the reagent diffuses into the sample can be used for TOF monitoring. For example, percentage diffusion, reagent concentration at the center of the tissue, or any other measure of the extent to which the reagent diffuses into the sample can be used to provide time-to-end information.

[0251] Furthermore, Time-of-Flight (TOF) technology can be used to determine the association between exposure to treatment reagents and the quality of histological specimens and / or downstream testing results. For example, it can establish the association between over- and under-treated samples and microscopic artifacts caused by overly soft or excessively fragile tissue. This information can be used to develop optimal practices and tissue handling guidelines for ideally treated samples, thus avoiding artifacts. In addition, quantitative studies to understand the potential impact of treatment on staining or antigen retrieval can be used to elucidate why and to what extent poorly fixed samples are more susceptible to artifacts caused by exposure to treatment reagents.

[0252] IV. Other embodiments

[0253] In another embodiment, a method is disclosed in which a first sample of a specific type, size, and shape undergoes TOF analysis to determine the optimal diffusion time for approximately 70% ethanol, and calculates the optimal time for all other steps in the tissue processing. The method may further include subjecting a second sample having a substantially similar shape to the first sample to a processing protocol based on the experimentally determined time for approximately 70% ethanol and the times calculated for other steps in the tissue processing. For example, an adjacent core from a tumor sample can be obtained and examined by TOF analysis to determine the optimal time for diffusion with approximately 70% ethanol, and then the second sample can be treated based on the experimentally determined protocol for immersion in approximately 70% ethanol and corresponding calculated protocols for other reagents such as approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin.

[0254] In another embodiment, a tissue processing system is disclosed, wherein the system includes: a controller (e.g., a microprocessor) having a database of protocol instructions stored thereon (or retrievable from external storage), the protocol instructions including tissue processing steps and times for groups of tissue samples of a specific type, shape, and size, or sharing a specific optimized processing protocol; and a user interface providing a user with optional protocols corresponding to each of the specific type, shape, and size tissue samples, wherein when the user selects a sample of a specific type, shape, and size, the controller controls the tissue processing system to process the tissue according to the selected protocol. In a particular embodiment, the disclosed tissue processing system includes multiple chambers that can process different groups of tissue samples according to different user-selectable protocols, the user-selectable protocols corresponding to groups of tissue samples of a specific type, size, and shape, or sharing a specific optimized processing protocol. In another specific embodiment, the system may include: a first part of the instrument including a TOF measurement chamber and a second part of the instrument including a tissue processor, wherein experimental TOF results obtained in the first part for a first sample having a specific type, size, and shape are used to automatically select (or display and be selected by the user) a tissue processing protocol for grouping samples of the corresponding type, size, and shape, or samples of the type, size, and shape represented by the first sample. Alternatively, the disclosed tissue processing system may include hardware necessary for TOF monitoring in one or more chambers to allow real-time monitoring of the process and to stop a specific processing step once a predetermined measurement of the amount or concentration of a specific reagent has been achieved in a pre-selected portion of the sample.

[0255] In another embodiment, a method is provided for determining the reagent concentration at (multiple) specific points within a sample immersed in a reagent at a given time. The method includes: simulating the spatial correlation of diffusion into the sample at multiple time points and for each of a plurality of candidate diffusivity constants to generate a model time-of-flight; and comparing the model time-of-flight with an experimental time-of-flight to obtain an error function, wherein the minimum of the error function yields a diffusivity constant for the sample. The method further includes: providing a plurality of candidate tissue porosities to generate the model time-of-flight using the diffusivity constants; and comparing the model time-of-flight with the experimental time-of-flight to obtain a second error function, wherein the minimum of the error function yields the porosity of the sample. Based on the sample's diffusivity constant and porosity, the concentration at one or more specific points within the sample at a given time can be calculated.

[0256] In some embodiments, optimized tissue processing protocols can be generated for tissue samples of different sizes, shapes, and types by comparing experimental results for determined reagent concentrations achieved at the center of tissue samples using the methods described above. Thus, in another embodiment, a modular tissue processor with separate chambers is provided, the separate chambers being used together to process samples with similar optimized processing protocols. For example, a single chamber may contain tissue samples of similar size and shape exhibiting similar porosity, or a mixture of samples with different porosities but different sizes. The advantage of such a tissue processor is that, unlike current practice, tissue processing time is no longer determined based on the sample requiring the longest processing time; instead, the process can be accelerated by grouping certain samples (typically one of the longest steps between surgical tissue removal and patient outcome), and it is ensured that such samples, which do not require such long processing times among various processing reagents, are not overexposed to such reagents and thus damaged.

[0257] This disclosure also provides a system comprising: an acoustic monitoring device that detects sound waves that have traveled through a tissue sample; and a computing device communicatively coupled to the acoustic monitoring device, the computing device being configured to evaluate the velocity of the sound waves based on time-of-flight, and including instructions that, when executed, cause the processing system to perform operations including: setting a range of candidate diffusivity constants for the tissue sample; simulating spatial correlation of reagents within the tissue sample for a first of a range of candidate diffusivity points at multiple time points; determining a modeled time-of-flight based on the spatial correlation; repeating the spatial correlation simulation for each of the plurality of diffusivity constants; and determining an error between the modeled time-of-flight for the plurality of diffusivity constants and the experimental time-of-flight for the tissue sample, wherein a diffusivity constant for the tissue sample is generated based on the minimum value of an error function of the error. The system further includes instructions that, when executed, cause the processing system to perform operations including: setting a candidate porosity range (e.g., between approximately 0.05 and approximately 0.50, for example, between approximately 0.05 and approximately 0.40, or between approximately 0.05 and approximately 0.30) for a tissue sample, comprising a plurality of candidate porosities; determining a second modeling time of flight based on a diffusion constant of the sample and a first of the plurality of candidate porosities; and determining a second error between the experimental time of flight and the second modeling time of flight; and repeatedly determining the second modeling time of flight and the corresponding second error for other porosities among the plurality of candidate porosities, wherein the minimum of the error identifies the porosity of the sample. In another specific embodiment, the system further includes instructions that, when executed, generate a spatial concentration distribution of a reagent within the sample at a specific time. In yet another specific embodiment, the system further includes instructions that, when executed, provide a reagent concentration at the center of the sample at a specific time. In yet another specific embodiment, such a reagent concentration can be used to terminate the soaking of the sample with the reagent when a predetermined concentration is reached at a specific point or area within the sample (such as at the center of the sample).

[0258] This disclosure applies to both biological and non-biological scenarios, thereby providing the ability to track the diffusion of any substance based on acoustic Time-of-Flight (TOF) curves. While the above operations provide fitting the TOF curve to a single exponential function or a summation of Bessel functions, depending on the context, a double exponential or quadratic function may be more appropriate. Therefore, the equations themselves can be modified, and the novel features disclosed herein will maintain their inventive spirit and scope when read by one of ordinary skill in the art.

[0259] Diffusion metrics and reagent concentration calculations are known to be useful for many applications, including component analysis. This system and method are contemplated for use in any system that utilizes diffusion metrics or reagent concentration measurements. In one particular embodiment, this system and method are applied to the field of monitoring the diffusion of fluids into porous materials.

[0260] In some embodiments, the porous material is a tissue sample. In many common tissue analysis methods, the tissue sample is diffused with a fluid solution. For example, Hine (Stain Technol. 1981 Mar; 56(2): 119-23) discloses a method for staining an entire tissue block by immersing the tissue sample in a hematoxylin and eosin solution after fixation and before embedding and cutting. Alternatively, fixation is often performed by immersing an unfixed tissue sample in a volume of fixative solution, allowing the fixative solution to diffuse into the tissue sample. As illustrated by Chafin et al. (PLoS ONE 8(1): e54138. doi:10.1371 / journal.pone.0054138 (2013)), failure to ensure that the fixative has diffused sufficiently into the tissue can compromise the integrity of the tissue sample. Therefore, in one embodiment, the system and method are used to determine sufficient time for the fixative to diffuse into the tissue sample. In such a method, the user selects the minimum fixative concentration to be achieved at a specific point in the tissue sample (such as the center of the tissue sample's thickness). Knowing at least the tissue thickness, tissue geometry, and the calculated true diffusivity, the minimum time required to reach the minimum relative (relative to the surrounding fluid) fixative concentration at the center of the tissue sample can be determined. The fixative will thus be allowed to diffuse into the tissue sample for at least the stated minimum time. However, to extend this to methods that can be used for real-time monitoring, the determination of tissue sample porosity, as disclosed herein, allows for the determination of the actual fixative concentration that needs to be achieved to ensure sample integrity. Therefore, based on the systems and methods disclosed herein, other techniques (such as radiolabeled tracking, intermediate IR assessment, and MRI) can be used to determine the appropriate time for a specific treatment using a particular reagent (such as a fixative).

[0261] In some embodiments, the systems and methods disclosed herein are used to run a dual-temperature immersion fixation method on tissue samples. As used herein, a “dual-temperature fixation method” is a fixation method in which the tissue is first immersed in a cold fixative solution for a first time period, and then heated for a second time period. The cold step allows the fixative solution to diffuse throughout the tissue without substantially causing cross-linking. Then, once the tissue has been sufficiently diffused throughout, the heating step results in cross-linking through the fixative. In some embodiments, the combination of cold diffusion and the subsequent heating step results in a tissue sample that is more fully fixed than when using standard methods. In some embodiments, the tissue sample is fixed by the following steps: (1) immersing an unfixed tissue sample in a cold fixative solution and monitoring the diffusion of the fixative into the tissue sample by 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 has been measured (fixation step). In some embodiments, the diffusion step is performed in a fixative solution at temperatures below 20°C, below 15°C, below 12°C, below 10°C, in the range of approximately 0°C to approximately 10°C, in the range of approximately 0°C to approximately 12°C, in the range of approximately 0°C to approximately 15°C, in the range of approximately 2°C to approximately 10°C, in the range of approximately 2°C to approximately 12°C, in the range of approximately 2°C to approximately 15°C, in the range of approximately 5°C to approximately 10°C, in the range of approximately 5°C to approximately 12°C, and in the range of approximately 5°C to approximately 15°C. In exemplary embodiments, the environment surrounding the tissue sample is allowed to be elevated in the range of approximately 20°C to approximately 55°C during the fixation step. In some embodiments, the fixative is an aldehyde-based crosslinking fixative, such as a solution based on glutaraldehyde and / or formalin. Examples of aldehydes commonly used for immersion fixation include:

[0262] Formaldehyde (standard working concentration of approximately 5% to approximately 10% formalin for most tissues, although higher concentrations such as approximately 20% formalin have been used in some tissues); glyoxal (standard working concentration of 17 to 86 mM); glutaraldehyde (standard working concentration of 200 mM).

[0263] solution Standard components Neutral buffered formalin 5-20% formalin + phosphate buffer (pH approximately 6.8) Formar Calcium 10% formalin + 10g / L calcium chloride Formarium salt solution 10% formalin + 9g / L sodium chloride Zinc formalin 10% formalin + 1g / L zinc sulfate Helly fixative 50 mL of 100% formalin + 1 L of aqueous solution containing 25 g / L potassium dichromate + 10 g / L sodium sulfate + 50 g / L mercuric chloride B-5 fixative 2 mL of 100% formalin + 20 mL of aqueous solution containing 6 g / L mercuric chloride + 12.5 g / L anhydrous sodium acetate Hollande solution 100mL 100% formalin + 15mL acetic acid + 1L aqueous solution containing 25g copper acetate and 40g picric acid Bouin solution 250mL 100% formalin + 750mL saturated aqueous picric acid + 50mL glacial acetic acid

[0264] Table 2.

[0265] In some embodiments, fixative solutions are selected from Table 2. In some embodiments, the aldehyde concentration used is higher than the standard concentrations mentioned above. For example, a high-concentration aldehyde-based fixative solution may be used, wherein the aldehyde concentration of the fixative solution is at least 1.25 times higher than the standard concentration used for fixing selected tissues for immunohistochemistry in substantially similar compositional cases. In some examples, the high-concentration aldehyde-based fixative solution is selected from: greater than about 20% formalin, about 25% formalin or greater, about 27.5% formalin or greater, about 30% formalin or greater, from about 25% to about 50% formalin, from about 27.5% to about 50% formalin, from about 30% to about 50% formalin, from about 25% to about 40% formalin, from about 27.5% to about 40% formalin, and from about 30% to about 40% formalin. As used here in this context, the term "approximately" should include concentrations that do not result in statistically significant differences in diffusion at 4°C as measured by 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 (2014-Mar-20).

[0266] In some embodiments, the dual-temperature fixation process is particularly useful for methods of detecting certain volatile biomarkers in tissue samples, including, for example, phosphorylated proteins, DNA, and RNA molecules (such as miRNA and mRNA). See PCT / EP2012 / 052800 (incorporated herein by reference). Thus, in some embodiments, fixed tissue samples obtained using these methods can be analyzed for the presence of such volatile markers. In some embodiments, a method for detecting volatile markers in a sample is provided, the method comprising: fixing tissue according to a dual-temperature fixation as disclosed herein, and contacting the fixed tissue sample with an analyte binding entity (such as FOXP3) capable of specifically binding to volatile markers. Examples of analyte-binding entities include: antibodies and antibody fragments (including single-chain antibodies) that bind to a target antigen; T-cell receptors (including single-chain receptors) that bind to an MHC:antigen complex; MHC:peptide multimers (that bind to a specific T-cell receptor); oligonucleotide aptamers that bind to a specific nucleic acid or peptide target; 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 a specific nucleic acid. For example, an immunohistochemical method for detecting phosphorylated proteins in a tissue sample is provided, the method comprising: contacting a fixed tissue obtained according to the aforementioned dual-temperature fixation method with an antibody specifically for phosphorylated proteins, and detecting the binding of the antibody to the phosphorylated protein. In some embodiments, an in situ hybridization method for detecting nucleic acid molecules is provided, the method comprising: contacting a fixed tissue obtained according to the aforementioned dual-temperature fixation method with a nucleic acid probe specifically for the nucleic acid of interest, and detecting the binding of the probe to the nucleic acid of interest.

[0267] The foregoing disclosure has presented exemplary embodiments of this disclosure for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the exact forms disclosed. In view of the foregoing disclosure, many variations and modifications of the embodiments described herein will be apparent to those skilled in the art. The scope of this disclosure will be defined solely by the appended claims and their equivalents.

[0268] Furthermore, in describing representative embodiments of this disclosure, the specification may have presented the methods and / or processes of this disclosure as a specific sequence of steps. However, the methods or processes should not be limited to the specific sequence of steps described herein in a sense that they are independent of the specific order of steps set forth herein. Other sequences of steps may be possible, as will be appreciated by those skilled in the art. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Additionally, the claims concerning the methods and / or processes of this disclosure should not be limited to the execution of the steps in the written order, and those skilled in the art will readily appreciate that the sequence may be varied while remaining within the spirit and scope of this disclosure.

[0269] References

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Claims

1. A method for processing tissue samples, comprising: (a) For each of a plurality of first tissue samples having different tissue types, sizes and / or shapes, a first lookup table is obtained comprising a plurality of first processing times, wherein each of the plurality of first processing times comprises a time period sufficient to allow a predetermined amount of first processing fluid to diffuse into the first tissue sample of the plurality of first tissue samples having a specific tissue type, size and / or shape, and wherein each of the plurality of first processing times is derived from time-of-flight analysis. (b) Obtain a second tissue sample; (c) Select a first processing time from the plurality of first processing times in a first lookup table based on the tissue type, size and / or shape of the obtained second tissue sample; and (d) The second tissue sample obtained by treating it in the first processing fluid for a processing period derived from the selected first processing time. Each of the plurality of first processing times is obtained in the following manner: Receive information related to a tissue sample to determine the velocity of sound in the tissue; Simulate the spatial correlation of relative fixative or reagent concentrations for various times and model diffusion constants to generate time-varying TOF signals and output model decay constants; The actual TOF signal of the tissue is determined, the spatial average value is calculated, and an experimental attenuation constant is generated, which depends on the tissue characteristics and the input from the acoustic monitoring device. Comparing experimental and modeled TOF data, the diffusion rate constant of the tissue sample is determined based on the minimum of the comparison error function. The diffusion rate constant determined in the modeling module, together with the candidate porosity values ​​for the tissue sample, is used to generate a second model TOF signal. A second comparison is performed between a second model TOF signal and experimental TOF data based on a determined diffusivity constant and the candidate porosity of the sample. The porosity of the tissue sample is determined based on the minimum value of the error function of the second comparison between the experimental TOF data and the model TOF signal generated using the determined diffusivity constant. The concentration of the reagent in the sample at a specific spatial and temporal point is calculated based on the experimental TOF signal, the determined diffusivity constant, and the determined porosity.

2. The method of claim 1, wherein selecting the first processing time based on the tissue type, size, and / or shape of the obtained second tissue sample comprises: The tissue type, size, and / or shape of the obtained second tissue sample are associated with the tissue type, size, and / or shape of one of the first tissue samples among the plurality of first tissue samples in the first lookup table.

3. The method according to claim 1, further comprising: Obtain a third tissue sample that has a similar tissue type, size, and / or shape to the second tissue sample obtained therefrom.

4. The method according to claim 3, further comprising: The second and third tissue samples are batch processed together based on the selected first processing time.

5. The method of claim 3, wherein the second and third tissue samples obtained are derived from the same tumor sample.

6. The method of claim 3, wherein the second and third tissue samples obtained are derived from different patients.

7. The method of claim 1, wherein the first processing fluid is selected from the group consisting of about 70% ethanol, about 90% ethanol, and about 100% ethanol.

8. The method according to claim 1, further comprising: A second treatment time is determined that is sufficient to allow a predetermined amount of the second treatment fluid to diffuse into the second tissue sample.

9. The method according to claim 8, further comprising: The second tissue sample is treated in the second processing fluid for a determined second processing time.

10. The method of claim 8, wherein the second processing fluid comprises 100% xylene.

11. The method of claim 8, wherein the second processing time is determined based on the selected first processing time and a predetermined functional relationship for calculating the second processing time from the first processing time, wherein the predetermined functional relationship represents the correlation of a plurality of data pairs, each data pair including first data and including second data, the first data indicating a processing time sufficient to diffuse a predetermined amount of first processing fluid into a particular type of tissue sample, and the second data indicating a processing time sufficient to diffuse a predetermined amount of second processing fluid into a particular type of tissue sample.

12. The method according to claim 1, wherein, Each of the first processing times includes: (a) When one of the first tissue samples is immersed in the first processing fluid, an acoustic signal is transmitted through one of the first tissue samples; and (b) Determine one or more of the following: (i) When one of the first tissue samples is immersed in the first processing fluid, the time taken for the measured TOF signal to change by a predetermined change in the decay time of one of the first tissue samples is observed. (ii) When the first sample is immersed in the first processing fluid, the time taken for the measured TOF signal to undergo a predetermined change in the attenuation amplitude of one of the first tissue samples is observed. (iii) When one of the first tissue samples is immersed in the first treatment fluid, the time taken to observe a predetermined change in the percentage diffusion calculated based on the measured TOF signal through one of the first tissue samples, or (iv) The time taken to observe the predetermined change in reagent concentration at the center of the first tissue sample, calculated based on the measured TOF signal, when one of the first tissue samples is immersed in the first treatment fluid.

13. A system comprising: (a) Organizational processing system; (b) A processor communicatively coupled to a tissue processing system and a memory having a database thereon of protocol instructions for performing the method of any one of claims 1-12, and at least one of the following: (i) processing time for tissue samples with different tissue types, shapes and / or sizes, and / or (ii) processing time for groups of tissue samples that share substantially similar processing protocols and have substantially similar tissue types, shapes and / or sizes; and (c) A user interface that provides a data input function for the user, wherein the data input function allows the user to input the tissue type, shape and / or size of the test tissue sample or select the group to which the test tissue sample belongs based on the tissue type, shape and / or size of the test tissue sample, wherein when the type, size and / or shape of the test tissue sample is input or the group to which the test tissue sample belongs is selected, the processor controls the tissue processing system to process the test tissue sample according to the protocol instructions stored in the database for the input tissue type, shape and size or the selected group.

14. The system according to claim 13, wherein, The tissue processing system is configured to process multiple different test samples in parallel according to different protocol instructions and / or multiple different test sample groups sharing essentially similar processing protocols.

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