Systems and methods for sample processing

Abstract

To quantify the degree to which a sample is dehydrated, cleared or embedded in processing a tissue sample.SOLUTION: A method and a system are described for processing tissues according to particular processing protocols that are established based on time-of-flight measurement as a processing fluid is diffused into a tissue sample. In one embodiment, a measurement of time it takes to diffuse about 70% ethanol into a tissue sample is used to predict time it will take to diffuse other processing fluids into the same or similar tissue samples. Advantageously, the disclosed method and system can reduce the overall processing time and help ensure that only samples that require similar processing conditions are batched together.SELECTED DRAWING: Figure 40D

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] 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 by reference herein in their entireties.

[0002]

[0002] The present disclosure relates generally to systems and methods for ensuring that biological samples are properly processed for analysis. In particular, the present disclosure relates to systems and methods for ensuring that cellular samples are properly processed for microscopic analysis. [Background technology]

[0003]

[0003] Proper histological staining of tissue biopsies remains the gold standard for clinical diagnosis. Before a tissue sample can be examined using light microscopy, it must first be fixed, processed, and cut into thin slices, which are then placed on microscope slides and stained to enhance morphological features to highlight the presence of diagnostic markers. Current clinical practice involves placing specimens in a fixative such as neutral buffered formalin (NBF) after surgery. After diffusing into the tissue, formaldehyde stabilizes the tissue, preventing further deterioration by cross-linking the tissue's internal biological structures and hardening the sample for downstream microsectioning, and protecting the tissue from subsequent processing chemicals. The sample is then dehydrated through a graded series of ethanol solutions of increasing concentration, thereby gradually removing all aqueous solutions from within the tissue. Because paraffin and ethanol are largely immiscible, the tissue is placed in a series of detergents, such as xylene, to remove the ethanol and prepare the sample for infiltration by the embedding medium. The most common embedding medium is heated liquid paraffin, which is infiltrated into the sample and then rapidly cooled to provide support for microsectioning and long-term preservation of the specimen.

[0004]

[0004] Fixation and processing of histological specimens attempt to preserve the tissue in a state representative of how it was removed from the subject, but these steps are not without consequences. Incomplete ethanol dehydration or xylene washes impair the ability of paraffin to infiltrate the sample, softening the specimen and making it difficult to section. Conversely, overexposure to dehydrating and detergent agents can make the tissue stiff and brittle, resulting in microsectioning artifacts such as tissue tearing, microchattering, or the so-called "Venetian blind" effect. Aggressive dehydrating chemicals such as ethanol act by washing away free water and stripping bound water molecules from biomolecules. However, because water insulates these molecules from each other, when water is removed, adjacent molecules can be brought close by electrostatic forces, resulting in tissue shrinkage and morphological deformation. Care must be taken to increase the concentration of the ethanol solution; otherwise, deformation of cell membranes can also occur. Short processing protocols, or alternatively the use of exhausted reagents, can also result in a loss of structural detail.

[0005]

[0005] There is considerable evidence that the choice of fixative, fixative temperature, length of fixative time, and other preanalytical variables such as warm and cold ischemia can significantly alter the antigenicity of a sample. Unfortunately, the impact that subsequent processing steps have on tissue integrity and antigen retrieval is poorly understood. However, there is growing evidence that processing steps can actually affect staining quality. For example, incomplete dehydration and over-dehydration correlate with weaker immune recognition of target cells and increased background signal. Whereas thorough dehydration and rehydration have resulted in improved staining for some targets, such as L26 and kappa. The specific reagents used for dehydration and washing, in addition to the temperature of the reagents, can also affect the intensity and prevalence of immunohistochemistry (IHC) staining. Several studies have reported that longer dehydration times result in better RNA and protein preservation. Even paraffin can affect staining quality, as low-temperature wax reportedly produces better IHC results and less fragmented RNA.

[0006] Furthermore, processing steps are a confounding factor in antigen retrieval, and a synergistic effect appears to exist between the degree to which a sample is fully 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 may actually be important for antigen retrieval. This finding also appears to be supported by the more amenable nature of heat-induced antigen retrieval. Ethanol acts to remove N-hydroxymethyl adducts by binding additional amines before they have a chance to form stable crosslinks. Thus, in the case of over-fixed or insufficiently crosslinked samples, ethanol may prevent the sample from being fully crosslinked and stabilized. Inadequately fixed tissues are also particularly susceptible to degradation as ethanol removes bound water molecules, causing hydrophobic reversal of the tissue's biostructure and resulting in problems such as morphological distortion, faded nuclei, poor chromatin patterns, and abnormal collagen color. Conversely, properly crosslinked samples undergo little distortion upon processing.

[0007] Current clinical tissue processing is performed in batches using tissue processors that fix and process dozens of tissue cassettes simultaneously. Typically, clinical specimens are grouped together, with all needle-core biopsies processed using a rapid protocol and larger samples processed using separate, longer protocols. Group processing, especially with a rapid protocol, allows samples to diffuse quickly, while slower-diffusing tissues are at risk from overexposure to dehydration and cleaning chemicals. Furthermore, fixation and processing are slow processes, typically creating a bottleneck in the preanalytical phase. For example, a small 3 mm biopsy is recommended for 12 hours of optimal processing, while larger specimens that fit into standard-sized histology cassettes may require several days to be optimally prepared for microtomy and may require additional downstream staining assays. Furthermore, batch-mode processing times are highly variable and inherently laboratory-specific, which can lead to preanalytical variations from tissue processing steps. Techniques that can expedite the processing of histological specimens without compromising the quality of the tissue or biomarkers have broad applicability in current practice.

[0008]

[0008] Currently, no techniques exist that can quantify the extent to which a sample is dehydrated, washed, or embedded, either statically or dynamically. Summary of the Invention [Problem to be solved by the invention]

[0009] One embodiment of the present invention relates to systems and methods for, for example, sample processing. [Means for solving the problem]

[0010]

[0009] Disclosed herein are systems and methods for time-of-flight (TOF) monitoring of the diffusion rates of tissue processing fluids, e.g., formalin, as well as graded ethanol and xylene, into several different types of tissue. The disclosed systems and methods are used to measure, monitor, and / or predict the extent to which a sample is dehydrated, cleared, and embedded.

[0003] These systems and methods can be used to minimize the adverse effects that such processing has, for example, on overall tissue quality and downstream immune recognition of biomarkers. Furthermore, in some embodiments, the disclosed systems and methods can serve to expedite the time required to process tissue and provide a guide for standardizing the processing of histological specimens in a fully traceable and / or repeatable pre-analytical workflow.

[0011]

[0010] Surprisingly, it has been found that the diffusion rates of other chemicals used for tissue processing can be accurately predicted based on the rate at which 70% ethanol diffuses into a sample (e.g., obtained by measurement, predicted by a diffusion constant as described below, or by the time it takes to achieve a concentration of 70% ethanol at a particular point within the sample, such as at the center of the sample, as also disclosed below).

[0012]

[0011] In one aspect of the present disclosure, a method of processing tissue includes the steps of subjecting a first tissue sample to TOF analysis while the first tissue sample is immersed in a first processing liquid; determining a first processing time sufficient for a predetermined amount of the first processing liquid to diffuse into the first tissue sample; determining a second processing time sufficient for a predetermined amount of a second processing liquid to diffuse 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 processing time and the second processing time; and immersing the first tissue sample in the second processing liquid for the second processing time.

[0013]

[0012] In some embodiments, the step of determining a first processing time sufficient for a predetermined amount of the first processing liquid to diffuse into the first tissue sample includes determining one or more of: (i) the time it takes to observe a predetermined change in the decay time of the measured TOF signal passing through the first sample while the first sample is immersed in the first processing liquid; (ii) the time it takes to observe a predetermined change in the decay amplitude of the measured TOF signal passing through the first sample while the first sample is immersed in the first processing liquid; (iii) the time it takes to observe a predetermined change in the diffusion rate calculated from the measured TOF signal passing through the first sample while it is immersed in the first processing liquid; and (iv) the time it takes to observe a predetermined change in the reagent concentration at the center of the tissue calculated from the measured TOF signal passing through the first sample while it is immersed in the first processing liquid.

[0014] In some embodiments, the first processing liquid comprises about 70% ethanol, and the second processing liquid comprises one of 90% ethanol, about 100% ethanol, xylene, and paraffin. In some embodiments, the first processing time is used to determine second processing times for a plurality of second processing liquids. In some embodiments, the first processing liquid comprises about 70% ethanol, and the second processing time is determined for at least about 90% ethanol and xylene, and the method further includes immersing the first tissue sample in about 90% ethanol for the determined second processing time of about 90% ethanol and immersing the first tissue sample in xylene for the determined second processing time of xylene. In some embodiments, the first processing liquid consists of about 70% ethanol, and the second processing times are determined for about 90% ethanol, about 100% ethanol, xylene, and paraffin, and the method further includes sequentially immersing the first tissue sample in about 90% ethanol, about 100% ethanol, xylene, and paraffin for the second processing times determined for about 90% ethanol, about 100% ethanol, xylene, and paraffin, respectively.

[0015] In some embodiments, the method comprises a diffusion step of the first tissue sample that is substantially similar to that of the first tissue sample. The method may further include the steps of selecting a second tissue sample of a type, shape, and size having the characteristic, and immersing the second tissue sample in the first processing liquid for a first processing time and in the second processing liquid for a second processing time.

[0016] In some embodiments, the method further includes subjecting the first tissue sample to TOF analysis while the first tissue sample is immersed in the first processing liquid and determining a first processing time sufficient to diffuse a predetermined amount of the first processing liquid into the first tissue sample, the steps being 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, the selection of the first processing time being based on the type, size, and / or shape of the second tissue sample. Alternatively, also 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 the two or more second tissue samples having approximately similar first processing times. As used herein, "substantially similar" refers to parameters (such as time, size, shape, diffusion characteristics, processing time, protocol, etc.) that are within ±20% of each other. For example, two tissue samples may have substantially similar processing protocols if the processing time in a particular processing solution (such as 70% ethanol) differs between the two protocols by less than about ±20%, such as less than about ±10% or less than about ±5%.

[0017] In another aspect of the present disclosure, a system includes 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 can include stored therein a database of protocol instructions including tissue processing steps and other data, such as times or timing, for tissue samples of a particular type, shape, and / or size, and / or one or more groups of tissue samples of a particular type, size, and / or shape that share a substantially similar processing protocol. The system can include a user interface providing a data entry function to a user, the data entry function allowing the user to input a tissue sample type, shape, and size, or select a group to which the tissue sample type, shape, and size belongs, and upon input of the tissue sample type, size, and shape, or selection of a group to which the tissue sample type, shape, and / or size belongs, the processor controls the tissue processing system to process the tissue according to the protocol stored in the database for the input tissue type, shape, and size, or the selected group. In some embodiments, user input regarding the type, shape, and size of the tissue sample causes the processor to receive and display to the user on the user interface a group of tissue samples of a particular type, size, and shape that share substantially the same processing protocol as the tissue sample. In some embodiments, the system is configured to process multiple different samples and / or multiple different groups of samples that share a substantially similar processing protocol in parallel and according to different protocol instructions.

[0018] In another aspect of the present disclosure, there is a time-of-flight enabled tissue processing system comprising: a first bath in which a tissue sample may be immersed in a first processing liquid; an acoustic monitoring device configured to obtain TOF data from the tissue sample while the tissue sample is immersed in the first processing liquid; and a processor configured to receive the TOF data and calculate a first processing time sufficient for a predetermined amount of the first processing liquid to diffuse into the tissue sample, and further configured to calculate a second processing time sufficient for a predetermined amount of a second processing liquid to diffuse into the tissue sample, the second processing time being determined by a predetermined time interval between the first processing time and the second processing time. and a second bath in which the tissue sample is immersed in a second processing liquid, the processor monitoring the time of immersion of the tissue sample in the second processing liquid and alerting a user to remove the tissue sample from the second bath or causing the system to automatically remove the tissue sample from the second processing liquid in the second bath when the second processing time is reached. In some embodiments, the first processing liquid is about 70% ethanol and the second processing liquid is one of about 90% ethanol, about 100% ethanol, xylene, and paraffin. In some embodiments, calculating a first processing time sufficient for the predetermined amount of the first processing liquid to diffuse into the tissue sample includes determining one or more of: a time it takes to observe a predetermined change in decay time of a measured TOF signal passing through the first sample while the first sample is immersed in the first processing liquid; a time it takes to observe a predetermined change in decay amplitude of a measured TOF signal passing through the first sample while the first sample is immersed in the first processing liquid; a time it takes to observe a predetermined change in diffusivity calculated from the measured TOF signal passing through the first sample while immersed in the first processing liquid; and a time it takes to observe a predetermined change in reagent concentration at the center of the tissue calculated from the measured TOF signal passing through the first sample while immersed in the first processing liquid. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 illustrates a tissue processing system 100 for optimized tissue fixation according to an exemplary embodiment of the present subject disclosure. [Figure 2A]

[0019] FIG. 1 is a diagram showing an ultrasound scan pattern from a biopsy capsule. [Figure 2B] FIG. 10 depicts an ultrasound scan pattern from a standard size cassette. [Figure 2C]

[0020] FIG. 1 is a timing diagram for an exemplary embodiment of the subject disclosure. [Figure 3]

[0021] FIG. 1 illustrates a method for obtaining a diffusion coefficient for a tissue sample according to an exemplary embodiment of the present subject disclosure. [Figure 4]

[0022] FIG. 1 shows an alternative method for obtaining the diffusion coefficient for a tissue sample. [Figure 5]

[0023] Figure 5A is a graph showing the simulated concentration slope for the first time point during the experiment, and Figure 5B is a graph showing the simulated concentration slope for several time points during the experiment. [Figure 6]

[0024] Figure 6A is a graph showing the simulated amount of NBF concentration detected by ultrasound during the experiment, and Figure 6B is a graph showing the simulated TOF signal for the first candidate diffusion constant. [Figure 7]

[0025] 10 is a graph showing the time-varying TOF signal calculated for all potential diffusion constants. [Figure 8]

[0026] Figure 8A is a graph showing the trend of empirically calculated TOF collected from a 6 mm slice of a human tonsil sample, and Figure 8B is a graph showing the trend of spatially averaged TOF signals collected from a 6 mm slice of a human tonsil sample. [Figure 9]

[0027] Figure 9A is a graph of the calculated error function between simulated and empirically measured TOF signals as a function of candidate diffusion constants, and Figure 9B is a zoomed-in view of this error function. [Figure 10]

[0028] 1 is a graph showing the trend of the calculated TOF using the graphed modeled diffusion constants compared to the empirical TOF. [Figure 11]

[0029] Figure 11A is a graph showing the reconstructed diffusion constants for multiple tissue samples; and Figure 11B is a graph showing the reconstructed diffusion constants for multiple tissue samples. [Figure 12]

[0030] Figure 12A shows a system with a transmitter and receiver pair that measures TOF by phase shifting, and Figure 12B shows a system with a transmitter and receiver pair that measures TOF by phase shifting. [Figure 13]

[0031] 1 is a graph showing a model of diffusion of a reagent into a cylindrical object, such as a cylindrical tissue core; [Figure 14]

[0032] 1 is a graph showing a typical distribution of the diffusion rate of a reagent into the center of a tissue sample at about 3 hours and about 5 hours. [Figure 15]

[0033] 1 is a graph showing a typical ROC curve of staining quality (based on sensitivity and specificity) based on diffusion rate in the center of a tissue sample. [Figure 16]

[0034] 10 is a graph showing an exemplary graph of the difference in diffusivity in the center of a tissue sample between exposure to a reagent for about 3 hours and exposure to a reagent for about 5 hours. [Figure 17]

[0035] 1 is a graph showing raw data distribution of tonsil tissue volume porosity determined according to one disclosed embodiment. [Figure 18]

[0036] 1 is a graph showing box and whisker distribution of tonsil tissue volume porosity determined according to one disclosed embodiment. [Figure 19]

[0037] 1 is a graph showing a typical distribution of formaldehyde concentration in the center of a tissue sample as determined by one disclosed embodiment at about 3 hours and about 5 hours. [Figure 20]

[0038] 1 is a graph showing a typical ROC curve of staining quality (based on sensitivity and specificity) based on formaldehyde concentration in the center of a tissue sample. [Figure 21]

[0039] 1 is a typical graph of the difference in formaldehyde concentration in the center of a tissue sample between immersion in NBF solution for about 3 hours and immersion in NBF solution for about 5 hours. [Figure 22]

[0040] 1 is a graph showing the distribution of raw porosity for several tissue types as determined by one disclosed embodiment. [Figure 23]

[0041] 1 is a graph showing a set of box and whisker distributions of porosity for several tissue types as determined by one disclosed embodiment. [Figure 24]

[0042] 1 is a graph showing the distribution of diffusion constants for several tissue types. [Figure 25]

[0043] 1 is a graph showing a set of box and whisker distributions of diffusion constants for several tissue types. [Figure 26]

[0044] 1 is a graph showing the distribution of raw diffusivity at the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types. [Figure 27]

[0045] 1 is a graph showing a set of box and whisker distributions of diffusivity at the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types. [Figure 28]

[0046] 1 is a graph showing the distribution of raw formaldehyde concentrations in the center of tissue samples at 3, 5, and 6 hours for several tissue types. [Figure 29]

[0047] 1 is a graph showing a set of box and whisker distributions of formaldehyde concentrations in the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types. [Figure 30]

[0048] 1 is a graph showing the distribution of raw formaldehyde concentrations in the center of tissue samples for several tissue types after the indicated immersion times. [Figure 31]

[0049] 1 is a graph showing a set of box and whisker distributions of formaldehyde concentrations at the center of tissue samples for several tissue types after the indicated immersion times. [Figure 32]

[0050] FIG. 32 is a graph showing a labeled version of FIG. 31 separating tissue types that result in optimal staining after immersion in 10% NBF for about 5 or about 6 hours. [Figure 33]

[0051] 1 is a graph showing the raw distribution of formaldehyde concentration at the center of the tissue sample for all tissues after immersion in 10% NBF for approximately 6 hours. [Figure 34]

[0052] 1 is a graph showing the box and whisker distribution of formaldehyde concentrations at the center of tissue samples for all tissues after immersion in 10% NBF for approximately 6 hours. [Figure 35-1]

[0053] 1A-1C illustrate a method for obtaining the diffusion coefficient, porosity, and formaldehyde concentration at the center of a tissue sample according to an exemplary embodiment of the present subject disclosure. [Figure 35-2] 1A-1C illustrate a method for obtaining the diffusion coefficient, porosity, and formaldehyde concentration at the center of a tissue sample according to an exemplary embodiment of the present subject disclosure. [Figure 36A]

[0054] FIG. 1 is a perspective schematic diagram of a TOF-enabled tissue processing system. [Figure 36B]

[0055] FIG. 36B is a perspective schematic view of the acoustic monitoring device of FIG. 36A. [Figure 36C]

[0056] FIG. 1 shows a tissue processing cassette holding several pieces of tissue. [Figure 36D]

[0057] 1 is a graph showing several TOF traces obtained for a single piece of tissue collected by moving the sample relative to the transmitter and receiver portions of an acoustic monitoring device. [Figure 36E]

[0058] 1 is a graph showing the spatially averaged TOF signal observed for a sample (solid line) and its exponential curve fit (dashed line). [Figure 37A]

[0059] Graph showing raw TOF signal (top panel) and spatially averaged TOF signal (bottom panel) for a 6 mm kidney sample immersed in 10% NBF. [Figure 37B]

[0060] Graph showing raw TOF signal (top panel) and spatially averaged TOF signal (bottom panel) for a 6 mm kidney sample immersed in 70% EtOH. [Figure 37C]

[0061] Graph showing raw TOF signal (top panel) and spatially averaged TOF signal (bottom panel) for a 6 mm kidney sample immersed in 90% EtOH. [Figure 37D]

[0062] Graph showing raw TOF signal (top panel) and spatially averaged TOF signal (bottom panel) for a 6 mm kidney sample immersed in 100% EtOH. [Figure 37E]

[0063] Graph showing raw TOF signal (top panel) and spatially averaged TOF signal (bottom panel) for a kidney sample of approximately 6 mm immersed in xylene. [Figure 37F]

[0064] 1 is a graph showing the signal error averaged over all samples monitored for each process solution shown on the x-axis. [Figure 37G]

[0065] 1 is a graph showing the coefficient of determination (R-squared) averaged across all samples monitored for each process solution shown on the x-axis. [Figure 37H]

[0066] 1 is a graph showing the signal to noise ratio averaged over all samples monitored for each process solution indicated on the x-axis. [Figure 38A]

[0067] 1 is a graph showing absolute TOF signal over time for normal kidney tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 38B]

[0068] 1 is a graph showing absolute TOF signal over time for normal breast tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 38C]

[0069] 1 is a graph showing absolute TOF signal over time for normal colon tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 38D]

[0070] 1 is a graph showing absolute TOF signal over time for kidney cancer tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 38E]

[0071] 1 is a graph showing absolute TOF signal over time for breast cancer tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 38F]

[0072] 1 is a graph showing absolute TOF signal over time for adipose tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39A]

[0073] 1 is a graph showing normalized TOF signal over time for normal kidney tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39B]

[0074] 1 is a graph showing normalized TOF signal over time for normal breast tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39C]

[0075] 1 is a graph showing normalized TOF signal over time for normal colon tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39D]

[0076] 1 is a graph showing normalized TOF signal over time for kidney cancer tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39E]

[0077] 1 is a graph showing normalized TOF signal over time for breast cancer tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 39F]

[0078] 1 is a graph showing normalized TOF signal over time for adipose tissue in 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ. [Figure 40A]

[0079] Graph showing the distribution of all attenuation amplitudes grouped by reagent. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to 1.5× the interquartile range, and data outside the whiskers are represented by circles. [Figure 40B]

[0080] Graph showing distribution of attenuation amplitude by reagent and tissue type. Negative amplitude is increasing TOF, positive amplitude is decreasing TOF. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to 1.5 x interquartile range, and data outside the whiskers are represented by circles. Ca = cancer. [Figure 40C]

[0081] 1 is a graph showing the distribution of time required for 90% diffusion grouped by reagent. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to 1.5× the interquartile range, and data outside the whiskers are represented by circles. [Figure 40D]

[0082] 1 is a graph showing the distribution of time required for 90% tissue diffusion by reagent and tissue type. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to 1.5 x the interquartile range, and data outside the whiskers are represented by circles. Ca = cancer. [Figure 41A]

[0083] 1 is a graph showing the diffusion time of 90% ethanol versus 70% ethanol for several tissue types as labeled. Circles represent the average diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines are ±σ on the vertical axis, with specific tissue types labeled as such. [Figure 41B]

[0084] 1 is a graph showing the diffusion time of 90% ethanol versus 100% ethanol for several tissue types as labeled. Circles represent the mean diffusion time, horizontal dashed lines represent ±σ on the horizontal axis, and vertical solid lines represent ±σ on the vertical axis. [Figure 41C]

[0085] 1 is a graph showing the diffusion time of xylene versus the diffusion time of 70% ethanol for several tissue types as labeled. 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 42]

[0086] 10 is a graph showing a power regression fit (solid line) and its 99% prediction interval (dashed line) along with paired data graphing the time to 90% diffusion of 70% ethanol vs. 90% ethanol. [Figure 43A]

[0087] FIG. 43 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 42. [Figure 43B] FIG. 43 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 42. [Figure 43C] FIG. 43 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 42. [Figure 43D] FIG. 43 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 42. [Figure 44]

[0088] FIG. 10 is a graph showing a power regression fit (solid line) and its 99% prediction interval (dashed line) along with paired data graphing the time to 90% diffusion of 70% ethanol versus approximately 100% ethanol. [Figure 45A]

[0089] FIG. 45 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 44. [Figure 45B] FIG. 45 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 44. [Figure 45C] FIG. 45 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 44. [Figure 45D] FIG. 45 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 44. [Figure 46]

[0090] FIG. 10 is a graph showing paired data graphing the time to 90% diffusion of 70% ethanol versus pure xylene, along with a power regression fit (solid line), and its 99% prediction interval (dashed line). [Figure 47A]

[0091] FIG. 47 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 46. [Figure 47B]FIG. 47 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 46. [Figure 47C] FIG. 47 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 46. [Figure 47D] FIG. 47 is a graph showing statistical fit quality measures shown for the regressions shown in FIG. 46. DETAILED DESCRIPTION OF THE INVENTION

[0020]

[0092] It is also to be understood that, for any method claimed herein that includes more than one step or act, unless expressly stated to the contrary, the order of the method steps or acts is not necessarily limited to the order in which the method steps or acts are recited.

[0021]

[0093] As used herein, the singular terms "a," "an," and "the" include plural referents unless the context clearly indicates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly indicates otherwise. The term "includes" is defined inclusively, such that "including A or B" means including A, B, or A and B.

[0022]

[0094] As used herein, in the specification and claims, "or" should be understood to have the same meaning as "and / or," as defined above. For example, when separating items in a list, "or" or "and / or" should be interpreted as inclusive, i.e., as including at least one (but also including more than one) of the several elements or elements of the list, and optionally further unlisted items. "Only one of" Only terms clearly indicated to the contrary, such as "one of" or "exactly one of," or when used in the claims, "consisting of," refer to the inclusion of exactly one element of a number or list of elements. Generally, the term "or" as used herein does not include "either," "one of," "any ... When preceded by exclusive language, such as "only one of," or "exactly one of," it shall be construed exclusively as indicating exclusive alternatives (i.e., "one or the other, but not both"). When used in the claims, "consisting essentially of" shall have its ordinary meaning as used in the field of patent law.

[0023]

[0095] The terms "comprising," "including," "having," etc. are used interchangeably and have the same meaning. Similarly, "comprises," "includes," "has," etc. are used interchangeably and have the same meaning. In particular, each of these terms is defined consistent with the general U.S. patent law definition of "comprising," and therefore is to be interpreted as an open term meaning "at least the following," and is also to be interpreted as not excluding additional features, limitations, aspects, etc. Thus, for example, "a device having components a, b, and c" means a device that includes at least components a, b, and c. Similarly, the phrase "a method including steps a, b, and c" means that the method includes at least steps a, b, and c. Also, although steps and processes may be outlined herein in a particular order, those skilled in the art will recognize that the order of steps and processes may vary.

[0024]

[0096] As used herein, in the specification and claims, the phrase "at least one," in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of all elements specifically listed in the list of at least one element, and not excluding any combination of elements in the list of elements. This definition also allows for elements, whether related or unrelated to those specifically identified elements, to optionally be present other than those specifically identified in the list of elements to which the phrase "at least one" refers. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B," or equivalently, "at least one of A and / or B") can refer in one embodiment to at least one with no B and optionally including two or more As (and optionally including elements other than B); in another embodiment, to at least one with no A and optionally including two or more Bs (and optionally including elements other than A); in yet another embodiment, to at least one with optionally two or more As, and at least one with optionally two or more Bs (and optionally including other elements), etc.

[0025]

[0097] I. Technical Implementation

[0098] This disclosure presents systems and computer-implemented methods for calculating the diffusion constant (also known as the "diffusion coefficient") and porosity of a sample using acoustic time-of-flight (TOF)-based information correlated with a diffusion model to reconstruct spatial and temporal concentration profiles across a tissue sample. In some embodiments, the tissue preparation systems and methods disclosed herein can be adapted to monitor the diffusion of a fixative fluid into a tissue sample until a predetermined concentration level is reached. For example, as formalin penetrates the tissue, it displaces interstitial fluid. This fluid exchange at least partially changes the composition of the tissue volume, and this change can be monitored. As an example, if interstitial fluid and formalin respond differently to an introduced ultrasound pulse (i.e., each fluid has a discrete "speed of sound" characteristic), the output ultrasound pulse will accumulate a small transit time difference that increases as more fluid exchange occurs, i.e., as more formalin displaces interstitial fluid. This can be used to determine the phase difference accumulated due to diffusion based on the geometry of the tissue sample, model the effect of diffusion on TOF, and / or This allows for operations such as correlating results using analytical or post-processing algorithms to determine diffusion constants. Furthermore, the sensitivity of the disclosed TOF instrument is capable of detecting changes of less than 10 parts per million, potentially enabling more accurate characterization of diffusion constants and porosity. On the nanosecond TOF scale, all fluids and tissues have discrete speeds of sound, and therefore the disclosed operations are not limited to quantifying water diffusion but can be used to monitor the diffusion of all fluids into all tissues, including dehydration reagents (such as graded ethanols), detergents (such as xylene), and paraffin used for embedding tissue samples.

[0026]

[0099] The diffusion rate can be monitored by a system of acoustic probes based on the different acoustic properties of formalin-wetted tissue samples. Such a system for diffusion monitoring and empirical TOF measurements is described in further detail in U.S. Patent Application Publication Nos. 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 system suitable for diffusion monitoring and empirical TOF measurements is also described in an international patent application entitled "ACCURATELY CALCULATING ACOUSTIC TIME-OF-FLIGHT," filed December 17, 2015, the contents of each of which are hereby incorporated herein by reference in their entirety.

[0027]

[0100] Further examples of systems and methods suitable for TOF monitoring are described in PCT International Publication No. WO 2016 / 097163 and U.S. Patent Application Publication No. 2017 / 0284859, the contents of which are also incorporated herein by reference to the extent not inconsistent with the present disclosure. The referenced applications describe solid tissue samples that are contacted with a liquid fixative that migrates through the tissue sample, diffuses throughout substantially the entire thickness of the tissue sample, and are analyzed based on acoustic properties that are continuously or periodically monitored to assess the state and condition of the tissue sample throughout processing. For example, a fixative such as formalin, which has a bulk modulus greater than that of interstitial fluid, can significantly alter TOF as it displaces interstitial fluid. Based on the information obtained, fixation protocols can be adjusted to increase processing consistency, reduce processing time, improve processing quality, and the like. Acoustic measurements can be used to noninvasively analyze tissue samples. The acoustic properties of a tissue sample can change as a liquid reagent (e.g., a liquid fixative) migrates through the sample. The acoustic properties of a sample can change, for example, during a pre-soaking process (e.g., diffusion of a low-temperature fixative), fixation process, staining process, etc. During a fixation process (e.g., cross-linking process), the tissue sample becomes more heavily cross-linked, so the rate of acoustic energy transmission can change. Real-time monitoring can be used to accurately track the movement of fixatives 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 acoustic waves. Other examples of measurements include acoustic signal amplitude, attenuation, scattering, absorption, phase shift of acoustic waves, or a combination thereof.

[0028]

[0101] In some embodiments, the movement of the fixative through the tissue sample can be monitored in real time.

[0102] II. SYSTEMS AND METHODS

[0103] The term "time of flight" or "TOF" as used herein refers to the time it takes, for example, an object, particle, or acoustic, electromagnetic, or other wave to travel a distance through a medium. This TOF can be measured empirically, for example, by determining the phase difference between the phase of an acoustic signal emitted by a transmitter (the "transmitted signal") and the phase of an acoustic signal received by a receiver (the "received signal") that has passed through an object immersed in a fluid 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 a plurality of cells. Examples include tissue biopsy samples, surgical specimens, and the like. Examples of tissue samples include, but are not limited to, amniocentesis samples, and autopsy material. The sample may be mounted, for example, on a tissue sample slide.

[0029]

[0104] As used herein, the terms "biological sample," "biological specimen," "tissue sample," "sample," and the like refer to any sample containing biological molecules (such as proteins, peptides, nucleic acids, lipids, carbohydrates, or combinations thereof) obtained from any organism, including viruses. Other examples of organisms include mammals (such as humans; domestic animals, such as cats, dogs, horses, cows, and pigs; and laboratory animals, such as mice, rats, and primates), insects, annelids, arachnids, marsupials, reptiles, amphibians, bacteria, and fungi. Biological samples include tissue samples (such as tissue sections and needle biopsies of tissue), cell samples (such as Pap smears or blood smears, or cytological smears, such as samples of cells obtained by microdissection), or cell fractions, fragments, or organelles (such as obtained by lysing cells and separating their components by centrifugation or another method). 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 aspirate, earwax, milk, vaginal fluid, saliva, a swab (e.g., a buccal swab), or any substance containing a biological molecule obtained from a first biological sample. In some embodiments, the term "biological sample" as used herein refers to a sample (e.g., a homogenized or liquefied sample) prepared from a tumor or a portion thereof obtained from a subject. The sample may, for example, be mounted on a tissue sample slide.

[0030]

[0105] The term "porosity" refers to a measure of void (i.e., "empty") space in a material, and is the ratio of void volume to the total volume of the object as a percentage between 0 and 1, or between 0 and 100%. As used herein, "porous material" refers, for example, to a three-dimensional object having a porosity greater than 0.

[0031]

[0106] The term "diffusion coefficient" or "diffusion constant" as used herein refers to the proportionality constant between the molar flux, e.g., due to molecular diffusion, and the concentration gradient (or driving force for diffusion) of the object over which diffusion is observed. Diffusion rates are encountered in numerous equations in physical chemistry, e.g., in Fick's law. The higher the diffusion rate (of one substance relative to another), the faster the compounds / substances diffuse into each other. Typically, the diffusion constant of a compound in air is about 10,000 times that in water. Carbon dioxide in air has a diffusion constant of 16 mm 2 / s, and in water the diffusion constant is 0.0016 mm 2 / s.

[0032]

[0107] As used herein, the phrase "phase difference" refers to the difference, expressed in units of degrees or time, between two waves, for example, having the same frequency and referenced to the same point in time.

[0108] The phrase "biopsy capsule" as used herein refers to a container for, for example, a biopsy tissue sample. Typically, a biopsy capsule includes a mesh that holds the sample and allows liquid reagents, such as buffers, fixatives, or stains, to surround and diffuse into the tissue sample. The biopsy capsule can maintain the sample in a specific shape, which advantageously provides the sample with a shape that is easier to model and calculate using the disclosed methods and is therefore more suitable for use in the disclosed systems. The term "cassette" as used herein refers to, for example, a biopsy capsule or a container for a tissue sample not enclosed within a biopsy capsule. Preferably, the cassette is designed and shaped so that it can be automatically selected and moved, e.g., raised and lowered, relative to the beam path of the ultrasound transmitter / receiver pair, and further includes an opening that allows the movement of liquid reagents into and out of the cassette, and thus further the movement of the tissue sample held therein. For example, this movement can be performed by a robotic arm or another automated, movable component of the device to which the cassette is attached. In other embodiments, the cassette is used solely to contain the tissue sample, and the shape of the cassette can at least partially determine the shape of the tissue sample. By placing a rectangular block of tissue slightly thicker than the depth of the cassette into the cassette and closing the cassette lid, the tissue sample can be compressed and expanded to fill a larger portion of the cassette's internal space, thus transforming it into a thinner piece with a larger height and width but a thickness that roughly corresponds to the cassette's depth.

[0033]

[0109] In some embodiments, a system for calculating formaldehyde concentration or other reagents is disclosed, as described herein, comprising a signal analyzer including a processor and a memory coupled to the processor, the memory for storing computer-executable instructions that, when executed by the processor, cause the processor to perform operations including calculating a formalin concentration from a set of acoustic data.

[0034]

[0110] In some embodiments, the data input to the signal analyzer is an acoustic dataset generated by an acoustic monitoring system, where the acoustic dataset is generated by transmitting an acoustic signal such that the acoustic signal encounters a substance of interest and then detecting the acoustic signal after the acoustic signal encounters the substance of interest. Thus, in some embodiments, a system is provided that includes a signal analyzer and an acoustic monitoring system as disclosed herein. Additionally or alternatively, the system may include a signal analyzer and a non-transitory computer-readable medium that includes the acoustic dataset obtained from the acoustic monitoring system. In some embodiments, the acoustic data transmitted and received by the acoustic monitoring system is generated by a frequency sweep. As used herein, the term "frequency sweep" refers to a series of acoustic waves transmitted through a medium at fixed frequency intervals, such that a first set of acoustic waves is emitted through the medium at a fixed frequency for a first fixed duration, and a subsequent set of acoustic waves is emitted at fixed frequency intervals for a subsequent (preferably equal) duration.

[0035]

[0111] In some embodiments, a system may be provided that monitors the diffusion of a fluid into a porous material. In some embodiments, a system may be provided that includes: (a) a signal analyzer; (b) a non-transitory computer-readable medium containing an acoustic monitoring system and / or an acoustic dataset generated by the acoustic monitoring system; and (c) an apparatus for holding the porous material immersed in a volume of fluid. In some embodiments, the system may be provided that monitors the diffusion of a fixative into a fabric sample.

[0036]

[0112] In some embodiments, the formalin concentration or other reagent concentration is determined to characterize the extent to which the reagent penetrates a porous object. For example, this method can be used to monitor the staining process of objects such as fabrics, plastics, ceramics, tissues, or other objects, to monitor the fixation process, or to monitor other tissue processing steps such as dehydration, washing, and paraffin embedding.

[0037]

[0113] In some embodiments, the present disclosure provides an acoustic monitoring system for collecting acoustic datasets, the acoustic monitoring system comprising a transmitter and a receiver, the transmitter and receiver arranged such that an acoustic signal generated by the transmitter is received by the receiver and converted into a computer-readable signal. In one embodiment, the system comprises an ultrasound transmitter and an ultrasound receiver. As used herein, a "transmitter" is a device capable of converting an electrical signal into acoustic energy, and an "ultrasound transmitter" is a device capable of converting an electrical signal into ultrasonic acoustic energy. As used herein, a "receiver" is a device capable of converting acoustic waves into an electrical signal, and an "ultrasound receiver" is a device capable of converting ultrasonic acoustic energy into an electrical signal.

[0038]

[0114] Certain materials that are useful for generating acoustic energy from electrical signals are known as acoustic energy generators. The transmitter and receiver also serve to generate an electrical signal from the gas. Thus, the transmitter and receiver are not necessarily separate components, but the transmitter and receiver can be separate components. The transmitter and receiver can be positioned such that the receiver detects acoustic waves generated by the transmitter after the transmitted waves encounter the material of interest. In some embodiments, the receiver is positioned to detect acoustic waves reflected by the material of interest. In other embodiments, the receiver is positioned to detect acoustic waves transmitted through the material of interest.

[0039]

[0115] In some embodiments, the transmitter comprises at least a waveform generator operably linked to the transducer, the waveform generator configured to generate an electrical signal in communication with the transducer, and the transducer 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 frequency sweep, including, for example, the start and / or end frequencies, the step size between frequencies in the frequency sweep, the number of frequency steps, and / or the duration for transmitting each frequency. In other embodiments, the waveform generator is preprogrammed to generate one or more predetermined frequency sweep patterns. In other embodiments, the waveform generator may be configured to transmit both preprogrammed and customized frequency sweeps. The transmitter may also include a focusing element, which allows the acoustic energy generated by the transducer to be predictably focused and directed to a particular area of the object.

[0040]

[0116] In some embodiments, the transmitter can transmit a frequency sweep through the medium, which is then detected by the receiver and converted into an acoustic dataset that is stored in a non-transitory computer-readable storage medium and / or transmitted to a signal analyzer for analysis. If the acoustic dataset includes data representing the phase difference between the transmitted and received acoustic waves, the acoustic monitoring system can also 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. If the output of the phase comparator is an analog signal, the acoustic monitoring system can also include an analog-to-digital converter that converts the analog output of the phase comparator to a digital signal. The digital signal can then be stored, for example, in a non-transitory computer-readable medium or directly communicated to a signal analyzer for analysis. Alternatively, the transmitter can transmit acoustic energy at a particular frequency, and the signal detected by the receiver is stored and analyzed for its peak intensity.

[0041]

[0117] In some embodiments, a signal analyzer is provided that includes a processor and a memory coupled to the processor, the memory for storing computer-executable instructions that, when executed by the processor, cause the processor to calculate a formalin concentration based at least in part on an acoustic dataset generated by the acoustic monitoring system, as described above.

[0042]

[0118] The term "processor" encompasses all kinds of apparatus, devices, and machines that process data, including, by way of example, a programmable microprocessor, a computer, a system-on-chip, or a combination of more than one or the foregoing. An apparatus can comprise special-purpose logic circuitry, e.g., an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, an apparatus may also include code that creates an execution environment for the computer program in question, e.g., processor firmware, a protocol stack, a database management system, an operating system, a cross-platform routing environment, a virtual machine, or any combination thereof. The device and execution environment may implement a variety of different computing model infrastructures, such as a web service infrastructure, a distributed computing infrastructure, or a grid computing infrastructure.

[0043]

[0119] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted, declarative, or procedural, and it can be deployed in any form, such as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subprograms, or code portions). A computer program can be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0044]

[0120] The processes and logic flows described herein can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0045]

[0121] Processors suitable for executing a computer program include, by way of example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor that performs actions in accordance with the instructions and one or more memory devices for storing instructions and data. Typically, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical, or optical disks, or is operatively coupled to receive data from or transmit data to them, or both. However, a computer is not required to have such devices. A computer can also be incorporated into another device, such as a mobile phone, a personal digital assistant (PDA), a portable audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, by way of example, semiconductor memory devices such as 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. The processor and memory may be supplemented by, or incorporated in, dedicated logic circuitry.

[0046]

[0122] To interact with a user, each embodiment of the subject matter described herein can be implemented on a computer having a display device, e.g., an LCD (liquid crystal display), LED (light emitting diode) display, or OLED (organic light emitting diode) display, for displaying information to a user, as well as a keyboard and a pointing device, e.g., a mouse or trackball, by which a user can provide input to the computer. In some implementations, a touch screen can be used to display information and receive input from the user. Other types of devices may be used to interact with the user as well; for example, feedback sent to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic input, voice input, or tactile input. Additionally, the computer can interact with a user by sending documents to and receiving documents from a device used by the user, e.g., by sending a web page to a web browser on the user's client device in response to a request received from the web browser.

[0047]

[0123] Each embodiment of the subject matter described herein can be implemented in a computing system that includes a back-end component, e.g., a data server, or includes a middleware component, e.g., an application server, or includes a front-end component, e.g., a client computer having a graphical user interface or web browser through which a user can interact with an implementation of the subject matter described herein, or includes any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include local area networks (“LANs”) and broadband networks (“WANs”), interconnected networks (e.g., the Internet), and peer-to-peer networks (e.g., ad-hoc peer-to-peer networks).

[0048]

[0124] A computing system may include any number of clients and servers. Generally, clients and servers are remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server sends data (e.g., HTML pages) to the client devices (e.g., to display the data on the client devices and to receive user input from users interacting with the client devices). Data generated at the client devices (e.g., results of user interaction) can be received from the client devices at the server.

[0049]

[0125] In some embodiments, the signal analyzer accepts as input an acoustic dataset recorded from the test substance. The acoustic dataset represents at least a portion of a frequency sweep detected after the frequency sweep encounters the substance of interest. In some embodiments, the portion of the detected frequency sweep constitutes an acoustic wave reflected by the substance of interest. In other embodiments, the portion of the detected frequency sweep constitutes an acoustic wave that has passed through the substance of interest. Alternatively, the acoustic dataset represents a burst of acoustic energy at a single frequency that has reflected through or passed through the substance of interest.

[0050]

[0126] FIG. 1 illustrates one embodiment of a system 100 useful for tissue processing (e.g., for optimized tissue fixation, dehydration, cleaning, or embedding) according to an exemplary embodiment of the present subject disclosure. System 100 is implemented by a processor 105 coupled to a computer 101. The acoustic monitoring device 102 includes a plurality of processing modules or a memory 110 for storing logic instructions to be executed. The acoustic monitoring device 102 can include the aforementioned acoustic probe including one or more transmitters and one or more receivers. In some embodiments, the tissue sample can be immersed in a liquid fixative while the transmitters and receivers are in communication to detect the time of flight (TOF) of the acoustic waves.

[0127] In some embodiments, system 100 employs one or more processors 105 and at least one memory 110 that stores non-transitory computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to carry out instructions (or stored data) in one or more modules, including a tissue analysis module 111 that receives information about the tissue block via user input or electronic input and determines tissue characteristics such as the acoustic velocity of the tissue; a TOF modeling module 112 that simulates the spatial dependence of relative fixative or reagent concentrations over time and models diffusion constants to generate a time-varying ("expected" or "modeled") TOF signal and outputs a model attenuation constant; and a TOF analysis module 113 that determines the actual TOF signal of the tissue, calculates spatial averages, and estimates tissue characteristics (e.g., actual cell type, cell density, cell size, and sample preparation and / or sample staining). and a correlation module 114 that correlates (e.g., compares) the empirical TOF data with the modeled TOF data to determine a diffusion constant for the tissue sample based on a minimum of an error function of the correlation, generates a second model TOF signal using the diffusion constant determined in the modeling module 112 along with a candidate porosity value for the tissue sample, performs a second correlation between the empirical TOF data and the second model TOF signal based on the determined diffusion constant and candidate porosity for the sample, again using the correlation module 114, and determines the porosity of the tissue sample based on a minimum of the error function of the second correlation between the empirical TOF data and the model TOF signal generated using the determined diffusion constant, and calculates the concentration of the reagent in the sample at a particular point in space and time based on the determined diffusion constant and the determined porosity. These and other operations performed by these modules may result in quantitative or graphical results being output to a user or to the computer 101 .Thus, although not shown in FIG. 1, computer 101 may also include user input / output devices such as a keyboard, mouse, stylus, and display / touch screen.

[0051]

[0128] As mentioned above, modules comprise logic executed by processor 105. "Logic," as used herein and throughout these disclosures, refers to any information in the form of instruction signals and / or data that can be applied to affect the operation of a processor. Software is one example of such logic. Examples of processors are computer processors (processing units), microprocessors, digital signal processors, controllers, and microcontrollers, etc. Logic can be formed from signals stored in a computer-readable medium, such as memory 110, which in one exemplary embodiment can be random access memory (RAM), read-only memory (ROM), erasable / electrically erasable programmable read-only memory (EPROM / EEPROM), flash memory, etc. Logic can also comprise digital and / or analog hardware circuits; for example, hardware circuits comprise logical AND, OR, XOR, NAND, NOR, and other logical operations. Logic may also be formed from a combination of software and hardware. On a network, logic may be programmed on one server or a complex of multiple servers. A particular logical unit is not limited to a single logical location on the network. Also, the modules do not have to be executed in any particular order. Each module is executed when it needs to be executed. can call another module.

[0052]

[0129] In some embodiments, the acoustic monitoring device 102 can be retrofitted onto a commercially available dip-and-dunk tissue processor, such as the Lynx II by Electron Microscopy Sciences (RTM). A mechanical head designed using Solidworks® software can fit and seal a standard reagent canister. Once sealed, an external vacuum system can begin to degas the contents of the cassette, including the tissue along with bulk reagents. For smaller tissue samples, a cassette holder designed for use with either standard-sized histology cassettes, such as the CellSafe 5 by CellPath (RTM), or biopsy capsules, such as the CellSafe Biopsy Capsules by CellPath (RTM), may be utilized. Each holder securely holds the tissue to prevent sample slippage during the experiment. The cassette holder can be mounted on a vertical translation arm that slides the cassette holder in one direction. The mechanical head can be designed with two metal brackets, one on either side of the tissue cassette, housing five transmitting transducers and the other housing five receiving transducers, spatially aligned with their respective transmitting transducers. The receiving bracket can also accommodate pairs of transducers oriented orthogonally to the propagation axis of the other transducer. After each acquisition, the orthogonal sensors can calculate a reference TOF value to detect spatial and temporal variations in the fluid that significantly affect the speed of sound. Furthermore, at the end of each 2D acquisition, the cassette can be lifted and a second reference acquisition is obtained. These reference TOF values can be used to compensate for environmentally induced formalin variations. Environmentally induced variations in formalin or any other fixative can be due to, for example, temperature fluctuations, vibrations, etc., within a container containing a porous material.

[0053]

[0130] 2A and 2B show examples of ultrasound scan patterns from a biopsy capsule and a standard-sized cassette, respectively. The measurement and modeling procedures described herein for tissue examples can also be applied to other forms of porous material. Thus, while the present disclosure may demonstrate modeling in the context of a tissue sample, such examples are non-limiting and the present teachings may be applied to other materials, such as any porous material.

[0054]

[0131] As described herein, measurements from an acoustic sensor in an acoustic monitoring device can be used to track the change in TOF and / or rate of change of the acoustic signal through a tissue sample, including monitoring the tissue sample at different locations over time to determine diffusion or rate of diffusion over time.

[0055]

[0132] For example, the "different locations," also referred to as "candidate diffusion locations," may be locations within or on the surface of the 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. The relative movement may include moving the receiver and / or transducer to "scan" over the sample in a stepwise or continuous manner. Alternatively, the cassette can be moved from one location to another by a movable cassette holder.

[0056]

[0133] As shown in Figures 2A and 2B, for example, to image all the tissue in the cassette, the cassette holder can be successively raised vertically by ≈1 mm, and a TOF value is required for each new position. This process can be repeated until the entire open hole of the cassette is covered. Referring to Figure 2A, the tissue in the biopsy capsule 220 When imaging tissue in the standard-sized cassette 221 shown in FIG. 2B, signals are calculated from all five transducer pairs, resulting in the scan pattern shown in FIG. 2A. Alternatively, when imaging tissue in the standard-sized cassette 221 shown in FIG. 2B, the second and fourth transducer pairs can be turned off, and TOF values are acquired between the first, third, and fifth transducer pairs, which are located at the centers of the three central sections of the standard-sized cassette 221, respectively. Two tissue cores can then be arranged per column, one at the top and one at the bottom, allowing TOF traces from six samples (two rows by three columns) to be acquired simultaneously, significantly reducing run-to-run variability and increasing throughput. In this exemplary embodiment, the full width at half maximum of the ultrasound beam is approximately 2.2 mm.

[0057]

[0134] The acoustic sensor in the acoustic monitoring device can comprise a pair of spatially aligned 4 MHz focused transducers, such as the TA0040104-10 by CNIRHurricane Tech (Shenzhen) Co., Ltd. (RTM), with the tissue sample placed at their common focus. One transducer, designated the transducer, can emit an acoustic pulse that traverses the coupling fluid (i.e., formalin) and tissue and is detected by a receiving transducer.

[0058]

[0135] 2C shows a timing diagram for one exemplary embodiment of the subject disclosure. Initially, the transmitting transducer uses Analog The pulse train can be programmed using a waveform generator such as the AD5930 by Analog Devices (RTM). The pulse train can then be detected by a receiving transducer after traversing the fluid and tissue. The received ultrasound sine wave and the transmitted sine wave are compared using, for example, a digital phase comparator such as the AF8302 by Analog Devices. The output of the phase comparator provides a valid reading during the region of temporal overlap between the transmitted and received pulses. The output of the phase comparator is allowed to settle before being queried by an integrated analog-to-digital converter on a microcontroller such as the ATmega2560 by Atmel (RTM). The process can then be repeated at multiple acoustic frequencies across the bandwidth of the transducer to establish the phase relationship between the input and output sine waves over a frequency range. This acoustic phase frequency sweep is used directly to calculate the time of flight using a post-processing algorithm that is similar to an acoustic interferometer and can detect transit times with sub-nanosecond accuracy.

[0059]

[0136] In some embodiments, the "measured TOF" obtained for a particular time point and a particular candidate diffusion point, i.e., the "measured TOF value," is calculated from the phase shift measured between the transmitted ultrasound signal and the corresponding received ultrasound signal, whereby the beam path of the ultrasound signal intersects the particular candidate diffusion point, whereby the phase shift was measured at the particular time point.

[0060]

[0137] 3 illustrates a method for obtaining a diffusion coefficient for a tissue sample according to an exemplary embodiment of the present subject disclosure. The operations disclosed in connection with this embodiment can be performed by any electronic or computer-based system, including the system of FIG. 1. In some embodiments, these operations can be encoded on a computer-readable medium, such as a memory, executed by a processor, and result in an output that can be presented to a human operator or used for subsequent operations. Additionally, these operations can be performed in any order in addition to the order disclosed herein, provided that the spirit of the present subject disclosure is maintained.

[0061]

[0138] In some embodiments, the method can include calculating the acoustic velocity for the tissue sample (S330). This operation can include calculating the speed of sound in a reagent in which the tissue sample is immersed. For example, the distance d between the ultrasonic transducers can be calculated. sensor , i.e., the distance between the transmitting and receiving transducers can be accurately measured, and the ultrasonic The transit time t between the transmitter and the ultrasonic receiver reagent is measured, where the speed of sound in the reagent, r reagent teeth,

[0062]

number

[0063] It is calculated using

[0139] In some embodiments, the tissue thickness may be obtained by measurement or user input. A variety of suitable techniques are available for obtaining the tissue thickness, including ultrasonic, mechanical, and optical methods. Finally, the acoustic velocity may be calculated as:

[0064]

number

[0065] The phase lag is determined by obtaining the phase lag from the non-diffusing tissue (i.e., tissue sample to which fixative has not yet been applied) relative to the bulk reagent (e.g., fixative) using (S330).

[0140] In some embodiments, a specific equation is derived based on the known geometry of the tissue sample, and generally, this equation represents the speed of sound in a non-diffusing tissue sample (i.e., a tissue sample lacking a reagent, e.g., a fixative) at time t=0. In experimental embodiments, for example, the acoustic velocity in a tissue sample is determined by the distance (d) between two ultrasound transducers (also referred to herein as "sensors"), which are precisely measured, such as with calibrated calipers. sensor) can be calculated by first calculating the speed of sound in the reagent. In this example, the sensor separation distance is measured using calipers and the sensor separation distance d sensor = 22.4 mm. Next, the transit time (t reagent ) can be accurately recorded with the applicable program. In an experimental example, t for the bulk reagent of 10% NBF (Neutral Buffered Formalin) reagent = 16.71 μs. Then, the reagent (r reagent ) the speed of sound in

[0066]

number

[0067] It can be calculated as follows:

[0141] In this particular experiment, tonsillar specimens were prepared with a precise and standardized specimen thickness (d tissue A 6 mm histological biopsy core punch was used to ensure a tissue biopsy (t = 6 mm). The TOF difference (Δt) was measured between the acoustic sensors to determine the time of flight (t) in the presence of tissue. tissue+reagent ) and the absence of tissue (t reagent ), i.e., Δt=t tissue+reagent -t reagent Δt=16921.3-16709.7=211.6ns was calculated.

[0068]

[0142] In some embodiments, time t reagent is the signal from the transmitting transducer to the receiving transducer. The time of flight (TOF) is the time required for the ultrasound signal to traverse the distance to the target, whereby the signal passes through the reagent volume but not the tissue sample. This traversal time can be measured, for example, by placing a biopsy capsule having the same diameter as the tissue, e.g., 6 mm, between the two sensors and performing a TOF measurement on the signal passing through only the reagent and not the tissue.

[0069]

[0143] In some embodiments, time t tissue is the time required for an ultrasound signal to traverse the distance from the transmitting transducer to the receiving transducer, whereby the signal passes through a tissue sample that does not contain and is not surrounded by reagent. In some embodiments, the traversal time can be measured, for example, by placing a biopsy capsule between two sensors before adding reagent to the capsule and performing a TOF measurement on the signal passing through only tissue.

[0070]

[0144] In some embodiments, the time difference (or "TOF difference") Δt caused by the tissue in addition to the tissue thickness and the speed of sound in the reagent is calculated using the following equation, which is derived from the known geometry of the sample (e.g., cylindrical, cubic, box, etc.):

[0071]

number

[0072] Using the non-diffusion tissue (t tissue can be used to calculate the speed of sound at time t (t=0).

[0145] A modeling process is then performed to model the TOF for various candidate diffusion constants. In some embodiments, the candidate diffusion constants include a range 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 likely for the particular tissue or material under observation. In some embodiments, these evaluated candidate diffusion constants are fed into a modeling process (steps S332-S335), and a minimum of the error function is determined to obtain the true diffusion constant of the tissue (S337). In other words, the method tracks the difference between an empirically measured TOF diffusion curve and a series of modeled diffusion curves by varying the diffusion constant.

[0073]

[0146] For example, by selecting one of several candidate diffusion constants, the spatial dependence of the reagent concentration in the tissue sample can be calculated using the heat equation for a cylindrical object:

[0074]

number

[0075] Using the solution of reagent The simulation is performed based on the calculation of (S332).

[0147] where x is the spatial coordinate in the tissue depth direction, R0 is the radius of the sample, D is the candidate diffusion constant, t is time, J0 is a Bessel function of the first kind and zeroth order, J1 is a Bessel function of the first kind and first order, and α n is the location of the n-th root of the zeroth-order Bessel function, and c max is the maximum concentration of the reagent. In other words, the sum of the coefficients of each of these Bessel functions (higher order differential equations) is a constant, as a function of space, time, and velocity. This equation provides the diffusion constant, i.e., the diffusion constant. While this equation is specific to the cylindrical tissue samples disclosed in these experimental embodiments, the equation varies depending on the geometry or boundary conditions, and a solution to the heat equation for any geometry can provide the diffusion constant for that geometry. For example, the heat equation for an object having a spherical, cubic, or rectangular block shape can also be utilized in the disclosed methods.

[0076]

[0148] In some embodiments, this step is repeated for multiple time points (S333-S334) to obtain a time-varying TOF (S335) (corresponding to the expected reagent concentration, since the integral of the expected reagent concentration at a particular time point can be used to calculate the derivative of the speed of sound). For example, a determination is made as to whether the diffusion time is complete. In some embodiments, this diffusion time can be based on the type of hardware or system used. For each time interval T, steps S333, S334, and S332 are repeated until the modeled sample concentration is converted to a time-varying TOF signal and the modeling time is completed (S335).

[0077]

[0149] In an experimental embodiment, each candidate diffusion constant D used candidate is the following value: 0.01≦D candidate ≦2μm 2 / ms is included in the range.

[0078]

[0150] In some embodiments, the tissue sample forms the core of a cylindrical biopsy core punch and can therefore be well approximated by a cylinder. In some embodiments, the solution of the heat equation above is then used to determine the expected concentration of the reagent in the tissue sample (c reagent ) and calculate the solution, which represents the concentration of reagent across the tissue depth, for the first time point of the experiment, i.e., after 10 seconds of diffusion (based on the time interval between TOF acquisitions used in the system performing the disclosed experiments), is shown in Figure 5A. For example, a particular system may regularly measure a new TOF value at every several distinct spatial locations, also referred to herein as "pixels." Thus, each "pixel" may have an update rate that assigns a new TOF value, for example, every 10 seconds.

[0079]

[0151] Figure 5A shows the simulated concentration slope of 10% NBF into a 6 mm sample of tissue after 10 seconds of passive diffusion calculated from the heat equation in an experimental embodiment. These steps were also repeated every 10 seconds to determine the concentration of the reagent throughout the tissue during the experiment (8.5 hours long in the experimental embodiment), the results of which are shown in Figure 5B.

[0080]

[0152] Figure 5B shows the (“expected,” “modeled,” or “heat equation-based”) concentration of the reagent at every location in the tissue (horizontal axis) and every time. reagent A graph of (t,r) is shown (the curve moving upward).

[0081]

[0153] Referring back to Figure 3, the results of the reagent modeling steps (S332-S334) can be used to predict the contribution of the ultrasound detection mechanism to the ultrasound signal based on linearly building up a phase delay with respect to tissue depth.

[0082]

[0154] 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 “integrated expected” reagent concentration c detected is also referred to as the "detected reagent concentration" and may be calculated. In some embodiments, the "detected reagent concentration" is not an empirically detected value. Rather, it is a differential value generated by spatially integrating all expected reagent concentrations calculated for a particular time t and a particular candidate diffusion constant. In some embodiments, the spatial integration can cover, for example, a radius of the tissue sample.

[0083]

[0155] For example, the detected reagent concentration c detected teeth,

[0084]

number

[0085] It can be calculated using

[0156] In some embodiments, the integrated reagent concentration c detected is used to calculate the total amount of reagent at a particular time. For example, additional volume and / or weight information of the sample can be used to calculate the absolute amount of reagent. Alternatively, in some embodiments, the amount of reagent is calculated in relative units, for example, as a percentage value indicating, for example, the volume fraction [%] of the sample that has already been diffused by the reagent.

[0086]

[0157] After simulation (i.e., calculation based on the heat equation model), the detected concentration of the reagent for a given candidate diffusion constant and a given time point is then:

[0087]

number

[0088] The TOF signal can be converted into a TOF signal as a linear combination of non-diffusing tissue and reagent signals using (S335).

[0158] However, r tissue where (t=0) is the non-diffusing tissue speed of sound, and ρ is the tissue porosity, which represents the volume fraction of the tissue sample available for fluid exchange with the bulk reagent. This equation therefore models the change in TOF signal from diffusion as a linear combination of two separate sound velocities (tissue and reagent). Since the TOFs of the respective sound velocities of pure tissue on the one hand and pure reagent on the other can be easily determined empirically (e.g., by respective phase-shift-based TOF measurements), the amount of reagent already diffused into the sample at a particular time point can be easily determined.

[0089]

[0159] In some embodiments, the TOF contribution of a pure tissue sample (without the TOF contribution of bulk fluids such as sample buffer or tissue fluid) can be obtained by subtracting the TOF contribution measured for a tissue sample that includes and / or is surrounded by bulk fluid from the TOF contribution measured for an ultrasound signal across a corresponding inter-transducer distance filled only with bulk fluid.

[0090]

[0160] Figures 6A and 6B show a graph of the simulated, "detected," or "integrated" concentration of NBF due to ultrasound during the experiment (Figure 6A) and a graph of the simulated (or "expected") TOF signal for the first candidate diffusion constant (Figure 6B, where D = 0.01 μm / ms). The TOF signal in Figure 6B is calculated as the derivative of the integrated concentration of each of the reagents.

[0091]

[0161] In this regard, the method generally involves comparing a modeled (or "simulated" or "expected") TOF with the true diffusion time-of-flight (TOF) measured at different spatial regions of interest (ROIs), also called "candidate diffusion points," within a tissue sample. The modeled TOF is then correlated with the determined empirical TOF by determining the minimum of the error function to obtain the diffusion constant (S336). In this example, each modeled TOF for a particular diffusion constant selected within the range specified by (S322) is correlated with the empirical TOF (S336), and a determination is made as to 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 based on the correlation (S336) that the error is minimized (S337), the true diffusion constant is determined (S339), and the method ends.

[0092]

[0162] Figure 4 shows an alternative method, whereby all candidate diffusion constants are first used to perform modeling based on steps S446-S447, and after all diffusion constants are processed, correlation is performed (S448). A diagram of the time-varying TOF signal calculated for all potential diffusion constants is shown in Figure 7. For example, Figure 7 shows simulated TOF traces for an 8.5-hour experiment on a 6 mm tissue sample, with diffusion constants ranging from 0.01 to 2.0 μm / ms. In the embodiment of Figure 4, error minimization is performed within the true diffusion constant determination step S439.

[0093]

[0163] In any case, an empirical TOF must be determined for the correlation to occur. In some embodiments, the empirical 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 reagent subtracted to separate the contribution from active diffusion into the tissue. In some embodiments, the individual TOF trends are temporally smoothed by filtering. In some embodiments, these spatially distinct TOF trends are then spatially averaged to determine the average rate of diffusion of 10% NBF into the tissue.

[0094]

[0164] Figures 8A and 8B show the empirically calculated TOF trends collected from a 6 mm section of human tonsillar sample (Figure 8A) and the spatially averaged TOF signal representing the average rate and amount of fluid exchange of 10% NBF into the tissue (Figure 8B), respectively.

[0095]

[0165] In some embodiments, the average rate of diffusion into tissue is highly correlated to a single exponential signal (shown by the dashed line in FIG. 8B ),

[0096]

number

[0097] is obtained by

[0166] where A is the amplitude of the TOF in nanoseconds (i.e., the TOF difference between an undiffused and a fully diffused tissue sample), and τ experimental is the decay constant of the sample, which represents the time required for the TOF to decay to 37% of its amplitude, i.e., the time required to be decayed by 63%, and offset is the vertical offset of the given decay function above.

[0098]

[0167] This 63% is calculated as follows: At time t = τ, TOF(T) = Ae (-tau / tau) =Ae -1 =A / e=A / 2.72=0.37*A can be obtained by

[0099]

[0168] While it is assumed here that the TOF decreases as the concentration of reagent in the sample increases, this method is equally applicable to reagents that, upon diffusing into the sample, increase the measured TOF. In an experimental embodiment, for a 6 mm section of human tonsil, τ experimental = 2.83 hours. Thus, from empirically determined TOFs for successive time points, the decay constant of a tissue sample can be calculated, for example, by graphing the amplitude of the TOF signal over time, analyzing the graph to identify the offset, and solving the above solution for the decay constant.

[0100]

[0169] In some embodiments, error correlation (S336 in FIG. 3, S448 in FIG. 4) is performed to determine the error of the modeled ("expected") TOF versus the empirical TOF. Having the calculated TOF signal, the simulated TOF signal, and the empirical TOF signal, the difference between the two signals can be calculated (S337) to see if a candidate diffusion constant minimizes the difference between the two signals.

[0101]

[0170] In some embodiments, the error function may be, for example, the following:

[0102]

number

[0103] Error(D)=(τ simulated (D)-τ experimental ) 2 can be calculated in a set of different ways using

[0104]

[0171] In some embodiments, the first error function calculates the difference between a simulated (“modeled”, “expected”) TOF signal and an empirically measured TOF signal, point by point.

[0105]

[0172] In some embodiments, the second error function exclusively compares the rate of diffusion between the simulated and modeled TOF signals by calculating the sum of the squares of the differences between the respective decay constants. experimental can be obtained empirically, as discussed above. The "modeled," "expected," or "simulated" decay constant τ simulated can be obtained analogously from modeled ("expected") TOF signals at successive time points that also follow a decay function.

[0106]

[0173] Based on the output of the error function, the true diffusion constant can be determined (S339). The true diffusion constant can be, for example, D reconstructed =arg min(Error(D)) It is calculated as the minimum value of the error function as follows:

[0107]

[0174] This equation allows the determination of candidate diffusion coefficients that produce TOF signals as close as possible to the empirical data.

[0175] For example, with respect to the method shown in Figure 3, an error function can be determined for each candidate diffusion constant until the error is minimized (S337). Alternatively, in the method of Figure 4, correlation with empirical TOF can be performed after all candidate diffusion constants have been processed, in which case determining the true diffusion constant (S439) involves 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. The goal is to minimize the error between the modeled coefficients and the empirical coefficients as disclosed herein.

[0108]

[0176] Figures 9A and 9B show graphs of the calculated error function between the simulated and empirically measured TOF signals as a function of the candidate diffusion constant (Figure 9A, ΔD ≈ 10 e~5 μm 2 / ms), and a zoomed-in view of the error function (Fig. 9B). In an experimental embodiment, the minimum value of the error function is D = 0.1618 μm 2 The reconstructed constants were calculated to be in / ms. The validity of the reconstructed constants was tested and used to back-simulate the TOF trends. Figure 10 shows the TOF trends calculated using this diffusion constant and plotted along with the empirical TOF measured in a 6 mm slice of human tonsil. In Figure 10, the graph compares the empirically calculated TOF trend (dashed line) from a 6 mm slice of human tonsil with 10% NBF, with the D reconstructed =0.168μm 2 τ ≈ 1 / ms. experimental = 2.830 hours and τ simulated =2.829 hours.

[0109]

[0177] Furthermore, this same procedure was repeated for several specimens of 6 mm human tonsil samples, with successfully reconstructed diffusion constants for all samples, as shown in Figures 11A and 11B. Figure 11A shows the reconstructed diffusion constants for 23 specimens of 6 mm human tonsil. Line 1151 represents the mean. Figure 11B shows a boxplot illustrating the distribution of the reconstructed diffusion constants. Line 1152 represents the median, box 1153 extends from the 25th to 75th percentiles, and whiskers 1154 extend from the 5th to 95th percentiles. Overall, the 6 mm tonsil samples predicted by the algorithm have a mean diffusion constant of 0.1849 μm² / ms, which is relatively tightly distributed and results in a standard deviation of 0.0545 μm² / ms.

[0110]

[0178] 12A illustrates a system for monitoring the time of flight of an ultrasound signal according to an embodiment of the present disclosure. The ultrasound-based time-of-flight (TOF) monitoring system can include one or more pairs of transducers (e.g., TA0040104-10, CNIRHurricane Tech) for performing time-of-flight measurements based on the phase shift of the ultrasound signal. In the embodiment illustrated in FIG. 12A, the system includes at least one pair of transducers consisting of an ultrasound ("US") transmitter 902 and an ultrasound receiver 904, which are spatially arranged with respect to each other such that a tissue sample 910, placed within 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 can be contained within a sample container 912 (e.g., a standard tissue cassette such as CellPath's CellSafe 5 or a biopsy capsule such as CellPath's CellSafe Biopsy Capsules) that is filled with fixative. Phase-shift-based TOF measurements are performed before and after the biopsy capsule 912 is filled with fixative, while this solution slowly diffuses into the sample. One transducer, acting as a transmitter, sends out an acoustic pulse that traverses the tissue and is detected by the other transducer, acting as a receiver. The total distance between the two transducers that make up the transmitter-receiver transducer pair is referred to as "L." The total time required for an ultrasound signal to traverse the distance between the transmitter 902 and receiver 904 can be referred to as the signal's time-of-flight. The transmitter 902 can be centered at, for example, 4 MHz and support a frequency sweep range of 3.7 to 4.3 MHz.

[0111]

[0179] In some embodiments, the distance L is assumed herein to be known, at least approximately. For example, the transducer distance may be precisely measured (e.g., by optical, ultrasound-based, or other measurement techniques) or may be disclosed by the manufacturer of the acoustic monitoring system.

[0112]

[0180] In some embodiments, the transmitting transducer 902 is programmable using a waveform generator (e.g., AD5930 from Analog Devices) to transmit a sine wave (or "sine wave signal") at a defined frequency for a defined time interval, e.g., a few hundred microseconds. In some embodiments, this signal is detected by the receiving transducer 904 after traversing the fluid and / or tissue. In some embodiments, the received ultrasound signal 922 and the emitted (also called "transmitted") sine wave signal 920 are electronically compared using a digital phase comparator (e.g., AD8302, Analog Devices).

[0181] As used herein, the term "received" "signal" (or wave) refers to a signal having characteristics (such as phase, amplitude, and / or frequency) that are determined and provided by a transducer, e.g., a receiver 904, that receives the signal. Thus, the signal characteristics are determined after the signal has passed through a sample or any other type of material.

[0113]

[0182] As used herein, a "transmitted" or "emitted" "signal" (or wave) refers to a signal having characteristics (such as phase, amplitude, and / or frequency) that are characterized by a transducer, e.g., a transmitter 902, that emits the signal. In some embodiments, the signal characteristics are determined before the signal passes through a sample or any other type of material.

[0114]

[0183] For example, the transmitted signal can be characterized by signal characteristics determined by the transmitting transducer, and the received signal can be characterized by signal characteristics measured by the receiving transducer, whereby the transmitting transducer and receiving transducer are operably coupled to a phase comparator of the acoustic monitoring system.

[0115]

[0184] 12B shows a TOF determination for a pure reagent from which the speed of sound for a beam path across the pure reagent without a sample can be inferred. In this embodiment, one or more transducer pairs 902, 904 and the sample container 912 can be moved relative to one another. In some embodiments, the system includes a container holder that can reposition the container 912 so that the US beam traverses a region 914 of the container that contains only fixative but no tissue.

[0116]

[0185] At time A, when the tissue is not yet immersed in fixative, the TOF for the sound signal traversing the distance between the transducers is given by the measured phase shift ψexp, as illustrated in Figure 12A. In this case, the beam path traverses a reagent-free sample. Since L is known, the measured TOF can be used to calculate the speed of the sound signal traversing the distance in the presence of a non-diffusing sample.

[0117]

[0186] At time B, when the tissue is immersed in fixative, the TOF for the sound signal traversing the distance between the transducers is given by the measured phase shift ψexp. In this case, the beam path crosses a sample container containing only reagents and no sample (or crosses the sample container at a location where there is no sample). Since L is known, the measured TOF can be used to calculate the speed of the sound signal to traverse the distance where only reagents (and the sample container) are present in the beam path, i.e., where there is no sample.

[0118]

[0187] Time A and time B may represent the same point in time if an additional transducer pair is configured to perform two measurements in parallel.

[0188] III. Working Examples

[0189] An investigation of the disclosed method for determining reagent concentrations across space and time within a sample and across tissue sample types was conducted. As described above, samples were monitored during cryo-immersion in NBF using a TOF system, resulting in empirical TOF data extracted over time. After TOF analysis, the samples were heated to fix the tissue and then processed in a tissue processor to prepare paraffin blocks. In some embodiments, the blocks were sliced on a microtome, mounted on microscope slides, stained according to standard protocols, and, in some cases, read with a suitable slide reader to assess staining quality.

[0119]

[0190] Figure 13 shows a model of the diffusion of a reagent into a cylindrical object, such as a cylindrical tissue core. As can be seen, the reagent concentration increases rapidly at the edges of the tissue sample first, and (if at all) the concentration of the reagent at the center initially increases slowly, with a lag in the concentration change seen at the edges of the sample, then accelerating at a later point before beginning to slow again. In this model, TOF∝∫c(reagent) is.

[0120]

[0191] Compared to Figure 5B, the change in concentration over time is more variable than the change seen for the percentage diffused, which is not unexpected since the diffusion rate is an average measured across the entire sample, whereas the concentration change is location specific.

[0121]

[0192] Furthermore, the sample porosity is calculated using the following formula:

[0122]

number

[0123] Since it is proportional to A,

[0193] Once the diffusion constant is known, a simulated TOF curve can be calculated using the candidate porosity and compared to the empirical TOF curve to produce an error, which can be minimized. The error function can be, for example, the following:

[0124]

number

[0125] Error(D)-(τ simulated (porosity)-τ experimental ) 2 can be calculated in different ways using one of

[0126]

[0194] In some embodiments, the first error function calculates the point-by-point difference between a simulated (“modeled,” “expected”) TOF signal and an empirically measured TOF signal.

[0127]

[0195] In some embodiments, the second error function exclusively compares the diffusion rates between the simulated and modeled TOF signals by calculating the sum of the squares of the differences between the respective decay constants. experimental can be obtained empirically, as discussed above. The "modeled," "expected," or "simulated" decay constant τ simulated can be obtained analogously from modeled ("expected") TOF signals at successive time points that also follow a decay function.

[0128]

[0196] In some embodiments, the true diffusion constant can be determined based on the output of the error function. The true porosity is the minimum of the error function, e.g., ρ constructed =arg min(error(porosity)).

[0129]

[0197] In some embodiments, once the porosity of the sample is determined, the concentration of the reagent at a particular point in space and time can be calculated using the following formula:

[0130]

number

[0131] It can be calculated using

[0198] Figure 14 comparatively shows the typical distribution of the diffusion rate of formalin solution into the center of a tonsil tissue core sample (approximately 6 mm cylinder) at approximately 3 and 5 hours, showing that at approximately 3 hours, sample immersion results in fair staining, while at approximately 5 hours, immersion results in "ideal" staining. On average, samples exposed to approximately 3 hours of immersion reach a diffusion rate of approximately 52.6% at the tissue center, while samples exposed to 5 hours of immersion reach an average diffusion rate of approximately 76.9%. The approximately 95% prediction interval at approximately 5 hours indicates that the sample needs to be at least approximately 52.45% diffused at the center to achieve "ideal" staining, as determined by pathologist examination.

[0132]

[0199] For comparison, Figure 15 shows the receiver operating characteristics (ROC) of staining quality (sensitivity and specificity) based on diffusivity at the center of the tissue sample. The "characteristic" curve is shown. In this example, using the diffusivity at the tissue center yields an Area Under the Curve (AUC) of 0.8926 for predicting staining quality, based on the measurement of the percentage diffused at the tissue center.

[0133]

[0200] For comparison, Figure 16 shows a typical graph of the difference in measured diffusivity in the center of the tissue sample between 3 and 5 hours of exposure to the reagent (resulting in an average difference in the center of the tissue between 3 and 5 hours of diffusion of 24.3%).

[0134]

[0201] Turning now to the results obtained using the disclosed method of determining reagent concentration at specific spatial points within a tissue sample from TOF data, Figure 17 shows the raw data distribution of the determined tonsil tissue volumetric porosity for several samples. Figure 18 shows the corresponding box-and-whisker distribution of the data from Figure 17 for the determined tonsil tissue volumetric porosity. As can be seen, tonsil tissue, in particular, exhibits an average porosity of approximately 0.15.

[0135]

[0202] Figure 19 shows a typical distribution of formaldehyde concentration in the center of the tissue sample for tonsillar tissue core samples (approximately 6 mm cylinders) at approximately 3 and 5 hours, with 3 hours of sample immersion resulting in fair staining, while 5 hours of immersion results in "ideal" staining. On average, samples subjected to 3 hours of immersion reach a formaldehyde concentration of 92.3 mM in the tissue center, and samples subjected to 5 hours of immersion reach an average concentration in the tissue center of 137.5 mM. The 95% prediction interval at 5 hours indicates that to achieve "ideal" staining, as determined by pathologist inspection, samples should have achieved at least 91.07 mM formalin in the tissue center during fixation.

[0136]

[0203] Figure 20 shows the staining quality ( Figure 15 shows the ROC curve for sensitivity and specificity (AUC). The AUC in this case was 0.9256, which indicates the superiority of using formaldehyde concentration at the tissue center as an indicator of staining quality compared to using the percentage diffused at the tissue center as an indicator of staining quality (AUC - 0.8926), as shown in Figure 15.

[0137]

[0204] Similarly, Figure 21 illustrates the superiority of reagent concentration in the tissue center as an indicator of staining quality. Thus, Figure 21 shows a graph of the difference in formaldehyde concentration in the center of tissue samples between approximately 3 hours and approximately 5 hours of immersion in NBF solution. Over the entire period, the average difference in concentration is approximately 45 mM. Compared to the difference in the percentage diffused (24%; Figure 16), the difference in concentration in the tissue center between approximately 3 hours and approximately 5 hours is more dramatic at approximately 33% (45 mM / 137 mM x 100%), reflecting differences in reagent concentration that occur later during immersion, which can affect staining quality at the tissue center. Again, this illustrates the advantage of using a method that provides a measure (in this case, concentration) at a specific location and time within the sample volume, as opposed to an average measure across the entire sample volume (as is the case with the percentage diffused measurement alone).

[0138]

[0205] Having established that the disclosed method can be used to determine porosity for tonsil tissue, porosity was measured for approximately 10 different tissue types (approximately 80 samples), and the results are shown in Figure 22, which shows the distribution of raw porosity for several tissue types. Figure 23 is a graph showing a set of box and whisker distributions of porosity for several tissue types. As can be seen, for most tissue types, the average porosity (box line) is between about 0.1 and about 0.2, while skin has a much higher porosity of greater than about 0.3.

[0139]

[0206] For comparison, Figure 24 shows the distribution of determined diffusion constants for several tissue types, and Figure 25 is a graph showing a set of box-and-whisker distributions of diffusion constants for several tissue types. Compared to the average porosity determined among several tissue types, the diffusion constant is more variable.

[0140]

[0207] Figure 26 shows the distribution of raw diffusivities at the center of tissue samples at about 3, about 5, and about 6 hours as determined for several tissue types, and Figure 27 is a graph showing a set of box and whisker distributions of diffusivities at the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types. Figure 28 shows the distribution of raw formaldehyde concentrations as determined at the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types, and Figure 29 is a graph showing a set of box and whisker distributions of formaldehyde concentrations at the center of tissue samples at about 3, about 5, and about 6 hours for several tissue types. From a comparison of the raw data and box and whisker distributions based on measurements of diffusivities at the center of tissue with those based on measurements of reagent concentration, it can be seen that the data tend to be more tightly packed when concentrations are used.

[0141]

[0208] Figure 30 shows the distribution of raw formaldehyde concentrations in the center of tissue samples for several tissue types after the indicated immersion times, and Figure 31 is a graph showing a set of box-and-whisker distributions of formaldehyde concentrations in the center of tissue samples for several tissue types after the indicated immersion times. These results support the correlation of tissue center formaldehyde concentrations greater than about 90 mM (e.g., greater than 100 mM) with "ideal" staining, as earlier studies showed that fixation for at least about 6 hours (about 5 hours for tonsil) in the low-temperature step of a low-temperature + high-temperature fixation protocol ensures "ideal" staining. This result is further supported by microscopic analysis, where a suitable reader for determining the tissues shown in Figure 32 did indeed demonstrate "ideal (optimal)" staining after the indicated times.

[0142]

[0209] Figure 33 shows the raw distribution of formaldehyde concentration across all tissue types in the center of tissue samples after immersion in 10% NBF for 6 hours, and Figure 34 shows the box-and-whisker distribution of formaldehyde concentration in the center of tissue samples for all tissues after immersion in 10% NBF for 6 hours. The 90 mM (or 100 mM) formaldehyde concentration level for achieving "ideal staining" is supported across all tissue types. The difference between calculating formaldehyde concentration in the center of tissue based on TOF data and simply using the standard fixation time protocol is that although immersion for approximately 6 hours may not be sufficient to achieve ideal staining for samples larger than approximately 6 mm in diameter, a sufficient time to achieve at least 90 mM (or 100 mM) formaldehyde in the center of the tissue ensures "ideal" staining of the sample. Conversely, smaller samples (e.g., needle core biopsies) that may potentially be overfixed using standard fixation times of approximately 6 hours need only be processed until the concentration in the center of the tissue reaches at least approximately 90 mM, thus resulting in shorter overall analysis times.

[0143]

[0210] FIG. 35 illustrates one embodiment of the disclosed method for obtaining the diffusion coefficient, porosity, and formaldehyde concentration at the center of a tissue sample. In S430, the acoustic velocity of the sample is measured as described above in the context of FIG. 3. Also as in the embodiment of FIG. 3, actions and decisions S431, S432, S433, S434, S435, S436, S437, and S438 are performed to identify the diffusion constant. Once the diffusion constant is determined in S439, the range of porosity used to model the diffusion of the reagent solution into the sample is set in S440. This range may be set by default or may be input by the user. For example, based on the empirically determined porosity values described above with respect to FIG. 22, a range of 0.05 to 0.50 (or narrower) should cover most, if not all, tissue types. When a tissue type has been previously tested, a narrower range may be set; for example, the user can input the tissue type to provide an appropriate range of values for the model to explore. In S441, S442, and S443, the diffusion constant from S439 and the candidate porosity established in S440 are used to model the spatial dependence of the reagent over a series of time points T through T+n. This model built over a series of time points is then used to generate an expected TOF curve in S444, which is correlated with the empirical TOF curve in S445. In S446, the error between the expected TOF curve and the empirical TOF curve is checked to see if it is minimal. If so, in S448, the porosity of the candidate tissue is determined to be the porosity of the actual tissue. If not, in S447, the process is repeated with a second candidate porosity. Once both the diffusion constant (S439) and porosity (S448) are determined, a spatial model of the reagent concentration over time can be generated in S449. Once the spatial model of the reagent concentration over time is established, the concentration at a specific point in the sample at a specific time, such as the concentration at the center of the sample, can be extracted from the model.

[0144]

[0211] In a second set of experiments, the applicability of TOF measurements to monitor the tissue sample preparation workflow after the sample was fixed was tested. Tissue samples were obtained as anonymized material. Samples were freshly collected from surgical resections and from autopsy material when obtaining fresh surgical material was difficult. All specimens were cored with a 6 mm punch or cut to a maximum thickness of approximately 6 mm. In practice, due to the jelly-like nature of the tissue, samples between 4 and 7 mm thick were included in the study, but the majority of samples (85%) were estimated to be 6 mm thick. Overall, a total of 250 tissues were collected from eight different organs, including normal and cancerous breast, normal and cancerous colon, normal and cancerous kidney, normal and cancerous lung, liver, fat, skin, and tonsil. Not all tissues were monitored per reagent, as the study initially began with NBF. This was because we added monitoring of the 3x ethanol dehydration and, finally, the xylene wash. If tissue was lost, reagents accidentally evaporated, or the TOF instrument had errors, data could not be collected, and therefore not all reagents were successfully monitored for all tissues. Overall, 170, 113, 123, 98, and 31 tissue samples were monitored in NBF, 70% ethanol, 90% ethanol, 100% ethanol, and xylene, respectively.

[0145]

[0212] As previously described, a commercially available dip-and-dunk tissue processor (Lynx II, Electron Microscopy Sciences) was modified with acoustic monitoring technology. A custom-developed digital acoustic interferometry algorithm, described elsewhere, was used to detect the small acoustic phase delays resulting from fluid exchange within the tissue with subnanosecond accuracy (25). A pair of 4 MHz focused transducers was specially arranged, and the tissue sample was placed near their common focus. The transmitting transducer was programmed to emit a sinusoidal pulse that was detected by the receiving transducer after traversing the reagent and tissue. The received pulse was used to calculate the transit time. The tissue was held within a mesh biopsy cassette (CellSafe 5 Biopsy Cassette, CellPath USA) to allow for the measurement of multiple samples at once (see Figure 1d). The cassette holder was mechanically translated to allow for monitoring diffusion throughout the tissue sample with a spatial resolution of 1 mm. A baseline TOF value is obtained by measuring the TOF through the reagent alone, and this reference value is subtracted from the TOF, tissue exhibiting a phase delay separate from the tissue and compensating for environmentally induced reagent variations.

[0146]

[0213] FIG. 36A shows a perspective schematic diagram of one embodiment of a TOF-enabled tissue processing system 100, which includes an acoustic monitoring device 102 and several chambers 120 with baths holding processing chemicals, between which the acoustic monitoring device 102 can be moved along with the sample for the execution of a tissue processing protocol. In other embodiments, the acoustic monitoring device can be incorporated into the walls of certain baths, and the sample is moved in and out of the TOF bath and other baths according to the tissue processing protocol. In a specific embodiment, the TOF-enabled bath is a 70% EtOH bath, which advantageously allows for automatic configuration of subsequent processing steps based on the TOF behavior of tissue in 70% ethanol. As used herein, "bath" refers to any enclosure within which tissue processing steps are performed. Those skilled in the art will recognize that there are many different designs of tissue processors that can be modified or otherwise redesigned to include a TOF measurement volume within one or more baths. It is also conceivable that data obtained from a 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 tissue, or for groups of specific types and sizes of tissue that share similar processing requirements. The system could also provide users with guidance similar to the tissue types that a particular selected protocol might involve. 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 to batch with the first sample. In this way, only samples requiring similar processing time could proceed through processing at the same pace, thereby improving overall laboratory workflow efficiency.

[0147]

[0214] Figure 36B shows a perspective schematic view of an exemplary acoustic monitoring device 102. A tissue cassette 150 is movably held within the monitoring device between the ultrasound transmitter 130 and ultrasound receiver 140. Figure 36C shows the tissue processing cassette 150 holding several pieces of tissue 160. Relative movement between the ultrasound transmitter 130 and receiver 140 pair and the cassette 150 allows for TOF measurements at different portions of a single tissue sample or for TOF signals for different tissue pieces within the same cassette.

[0148]

[0215] For example, Figure 36D shows several TOF traces obtained for a single piece of tissue immersed in 10% NBF, collected by moving the sample relative to the transmitter and receiver portions of the 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 metric of the rate of fluid exchange, all individual TOF signals were averaged together to generate a single TOF curve representing the average diffusion rate throughout the specimen. Figure 36E shows the spatially averaged TOF signal observed for a single piece of tissue (solid line) and its exponential curve fit (dashed line). Because the speed of sound is faster in formalin than in the exchangeable fluid within the tissue, passive formalin diffusion into the tissue gradually increases the speed of sound in the sample. This increase in speed results in a gradual decrease in the acoustic transit time through the tissue. Consistent with the predictions of Fick's law, the sample initially undergoes a rapid change from a large concentration gradient, gradually trending toward the elimination of fluid exchange as diffusive equilibrium is achieved (see Figure 36E). This approach also mitigates the effects of substantial spatial heterogeneity within the tissue. Finally, in predicting TOF, changes during cold formalin diffusion correlate well with a single exponential decay, and therefore the average diffusion curve was fitted to a single exponential function using nonlinear regression. While the acoustic properties of tissue can change for various reasons during tissue processing, such as tissue shrinkage, deformation, or becoming less compressible, the TOF signal has previously been shown to be dominated by imperfect fields, due to the diffusion of fluids into and out of the tissue.

[0149]

[0216] In some embodiments, a custom tissue processing protocol was used to establish proof of concept that our TOF monitoring technology can detect tissue changes resulting from the diffusion of processing reagents. Instead of subjecting tissue to a single reagent multiple times, the custom protocol disclosed here subjects tissue to each reagent once over an extended period of time, allowing for the detection of continuous signals associated with the diffusion rate of each specific reagent. In sequential order, TOF monitoring was performed on each tissue sample in cold 10% NBF, 70% ethanol, 90% ethanol, 100% ethanol, and pure xylene. The times and temperatures for all reagents in the custom TOF protocol are shown in Table 1 below. Tissues were fixed in formalin using a two-temperature fixation method in which the tissue was initially placed in cold NBF to allow unrestricted diffusion of formaldehyde before being transferred to heated NBF to rapidly initiate crosslinking. Because the tissue was already in diffusive equilibrium with the bulk reagents and therefore no diffusion occurred, no TOF signal was reported in the heated NBF. In principle, TOF technology can detect the progression of paraffin embedding based on the differential sound speed between xylene and paraffin. However, for the actual study, it was not used to monitor paraffin embedding because the wax envelops the ultrasound transducer.

[0150] [Table 1]

[0151]

[0217] Table 1 shows the fixation and tissue processing steps for the custom TOF protocol used to study the diffusion kinetics of formalin, approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and pure xylene. s is the speed of sound, and Δc s is the difference in the speed of sound between the two following reagents. The speed of sound was referenced from various sources (25-27), and approximately 70% and 90% of the speed of sound were calculated for linear combinations of water and pure ethanol.

[0152]

[0218] The TOF system was used to study measures of fluid exchange from multiple reagents into several different types of tissue using a custom tissue processing protocol. Example TOF curves from small regions across a kidney sample are shown, and for all five monitored reagents, both the raw TOF signal (top panel) and the spatially averaged TOF signal (bottom panel) are shown in Figures 37A, 37B, 37C, 37D, and 37E. As the sample was moved to higher ethanol concentrations, the TOF through the tissue continuously increased (i.e., slowed down), as expected, since ultrasound travels more slowly in ethanol than in formalin. The reversal of TOF polarity for NBF diffusion and the decreasing amplitude of the ethanol signal were both consistent with expectations from the sound speed of these reagents. Finally, when the sample was moved from absolute ethanol to absolute xylene, the TOF decreased because the sound speed is greater in absolute xylene than in absolute ethanol. Consistent with previous findings in NBF, the TOF diffusion-based signals from approximately 70% ethanol, approximately 90% ethanol, approximately 100% ethanol, and absolute xylene are all well-fitted with a single exponential function, as can be seen in the bottom panels of Figures 37A, 37B, 37C, 37D, and 37E. The average deviation from each exponential fit (S error) across all tissues and reagents was at most 3.22 ns (see Figure 37F). on the order of hundreds of picoseconds. The adjusted R2 values from the TOF signals across all tissues and reagents were greater than 0.98 (see Figure 37G). The signal-to-noise ratios (SNRs) across all tissues and reagents are shown in Figure 37H. Overall, the TOF signals from all tissues and reagents correlated very well with a single exponential function, typically with adjusted R2 values greater than 0.98 and average deviations from the fit being only 1-2% of the TOF amplitude, as indicated by SNRs between 50 and 100 for all reagents. Of note is the high SNR for TOF measurements in approximately 70% ethanol, which is advantageously consistent with approximately 70% ethanol being the first reagent used in typical tissue processing protocols. As observed below, the diffusion rate (and therefore completion time) of subsequent tissue processing protocol steps can be predicted from the TOF measured in 70% ethanol.

[0153]

[0219] Figure 38A shows the absolute TOF signal over time for normal kidney tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 38B shows the absolute TOF signal over time for normal breast tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 38C shows the absolute TOF signal over time for normal colon tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 38D shows the absolute TOF signal over time for kidney cancer tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 38E shows the absolute TOF signal over time for breast cancer tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 38F shows the absolute TOF signal over time for adipose tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ.

[0154]

[0220] As expected given the sound speeds of these chemicals, TOF decreases at approximately 10% NBF, gradually increases with increasing concentrations of ethanol, and finally decreases in xylene. It should be noted that the amplitude of the three ethanol signals also decreases as the ethanol concentration increases, since replacing approximately 90% ethanol with approximately 100% produces a smaller difference in sound speed than replacing approximately 70% with approximately 90% ethanol. Interestingly, fat samples in xylene consistently showed an increase in TOF signal. While not fully explained, this counterintuitive result may be the result of xylene stripping of the fat sample, which has a faster sound speed than xylene, thus decreasing the speed 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 allows for quantitative tracking of the amount of fluid exchange occurring in the tissue specimen and the rate at which it is occurring.

[0155]

[0221] Figure 39A shows normalized TOF signals over time for normal kidney tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39B shows normalized TOF signals over time for normal breast tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39A shows the normalized TOF signal over time for normal colon tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39D shows the normalized TOF signal over time for kidney cancer tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39E shows the normalized TOF signal over time for breast cancer tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the average signal, and the shaded area is ±σ. Figure 39F shows the normalized TOF signal over time for adipose tissue in about 10% NBF, about 70% ethanol, about 90% ethanol, about 100% ethanol, and about 100% xylene. The solid black line represents the mean signal, and the shaded area is ±σ.

[0156]

[0222] The same example tissue types are shown in Figures 38A-38F and 39A-39F. The TOF signals were normalized to an 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, differences in the rate at which samples diffuse chemicals can be seen; for example, cancerous kidney and breast samples both have more variable processing rates compared to their normal counterparts; in general, the diffusion rate of ethanol appears to increase with concentration.

[0157]

[0223] Briefly, all data collected using the modified tissue processor were analyzed, along with the distribution of TOF decay times and amplitudes. Figure 40A shows the distribution of all decay amplitudes grouped by reagent. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to the 1.5x interquartile range, and data outside the whiskers are represented by circles. Figure 40B, on the other hand, shows the distribution of decay amplitudes by reagent and tissue type. Negative amplitudes are increasing TOFs, and positive amplitudes are decreasing TOFs. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to the 1.5x interquartile range, and data outside the whiskers are represented by circles (Ca = cancer). Because no accepted standards exist for the extent or rate at which histological specimens should be dehydrated in ethanol and washed in xylene, we choose to represent the time required for a sample to be 90% diffused. Figure 40C shows the distribution of time required for 90% diffusion grouped by reagent. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to the 1.5x interquartile range, and data outside the whiskers are represented by circles. Figure 40D shows the distribution of time required for 90% diffusion for tissue by reagent and tissue type. The solid line is the median, the solid box represents the 25th to 75th percentiles, the whiskers extend to the 1.5x interquartile range, and data outside the whiskers are represented by circles. Ca = cancer. It can be seen that while some samples, such as skin and fat, take longer to clear higher ethanol concentrations and xylene, the diffusion of approximately 70% ethanol is typically slower than that of the other three reagents. Again, there is considerable variability in the rate at which different organs clear the treatments. Notably, there is great variability in the processing of skin samples, along with how slowly fat samples clear all three ethanol solutions.

[0158]

[0224] The 90% diffusion time for a particular type of tissue in 90% ethanol, 100% ethanol, and xylene is compared to how long the sample needed to diffuse to 90% using 70% ethanol. Figure 41A shows the diffusion time for 90% ethanol versus 70% ethanol for several tissue types as labeled. 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; specific tissue types vary depending on their diffusion time. Figure 41B shows the diffusion time of about 90% ethanol versus about 100% ethanol for several tissue types, as labeled. 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 shows the diffusion time of xylene versus about 70% ethanol for several tissue types, as labeled. 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. Tissues that require longer times for adequate processing also tend to require longer times for all or most of the subsequent processing steps. While not a perfect correlation, tissue types that tend to diffuse about 70% ethanol slowly, such as fat and skin, and to a lesser extent breast, tend to take longer to diffuse about 90% ethanol, about 100% ethanol, and xylene. To this end, trends can be seen that show that the diffusion rates through the following reagents correlate with the diffusion rates in approximately 70% ethanol.

[0159]

[0225] Predictive criteria for optimal processing times were developed. The graphs in Figures 42, 44, and 46 show the mean and standard deviation of all collected data; however, only paired data points were included in the corresponding predictive statistical analyses shown in Figures 43A-43D, 45A-45D, and 47A-47D, respectively. The paired data indicate that diffusion times were successfully recorded for both reagents studied. This is important because sample-to-sample variability, even within a single tissue type, can be substantial and lead to misleading results. In Figures 42, 44, and 46, only paired data points are graphed for each of the three reagent pairs; each set of data points was empirically determined to best correlate with a power function using a variation constant. The accuracy of the curve fit was very robust, with variations from fits of 20, 29, and 8 minutes for approximately 90% ethanol, approximately 100% ethanol, and xylene, respectively. This demonstrates that the use of curve fitting with a power function and knowledge of the rate at which 70% ethanol diffusion occurs can accurately predict diffusion rates for other chemicals. Additionally, statistical analysis was performed for each empirical paired data set, and prediction intervals were calculated around each best-fit line. Prediction intervals are shown as dashed lines in all three graphs in Figures 42, 44, and 46. In Figures 42 and 44, the best-fit line for the 99% confidence interval is approximately 1 hour, indicating the rate at which the sample will diffuse approximately 70% ethanol, which can be used to predict how long it will take for 99% of the tissue sample to diffuse approximately 90% of the 100% ethanol within up to an hour. For xylene (Figure 46), the correlation is even stronger, so knowledge of the rate of diffusion of approximately 70% ethanol can be used to predict the time it will take for the sample to diffuse xylene within less than 30 minutes. This robust correlation demonstrates that once the diffusion rate for 70% ethanol is determined, the following steps can be accurately predicted:

[0160]

[0226] Figures 43A, 43B, 43C, and 43D show the statistical fit quality measures shown for the regression shown in Figure 42. Figures 45A, 45B, 45C, and 45D show the statistical fit quality measures shown for the regression shown in Figure 44. Figures 47A, 47B, 47C, and 47D show the statistical fit quality measures shown for the regression shown in Figure 46.

[0161]

[0227] Overall, 126 samples from eight different organs were monitored, in addition to cancer-affected breast, colon, kidney, and lung. Ethanol dehydration and xylene washes could be robustly characterized using acoustic TOF detection, with signal amplitude and rates of fluid exchange varying widely from sample to sample and among various tissue types. The functional form of fluid exchange from all treatment reagents correlated highly with a single exponential (R = 0.992 ± 0.02; deviation from fit of signal amplitude = 1.5% ± 1). In some embodiments, the technique allows for the assessment of the impact of treatment reagents on overall tissue quality and downstream immune recognition of cancer biomarkers such as FoxP3. This technique can be used to quantitatively assess the impact of fluid exchange on tissue quality. Furthermore, the disclosed method provides a guide toward accelerating tissue processing time and standardizing the processing of histological specimens in a fully traceable and repeatable pre-analysis workflow. This disclosure demonstrates real-time monitoring of formalin, graded ethanol, and xylene diffusion, dehydration, and washing in ex vivo tissue samples from multiple different tissue types using accurate acoustic TOF detection. The TOF diffusion trends from processing reagents are consistent with previous results monitoring NBF diffusion in tissue in real time using a modified tissue processor and accurate TOF calculation algorithms. This technique, combined with empirical measures of sample quality (e.g., pathologist scoring), allows the relative amount of fluid exchange, the rate of exchange, and even the concentration profile within the tissue to be correlated with the time required for optimal specimen processing for various sample types, sizes, shapes, etc., and can be extended to other tissue sample shapes using heat equations for different sample shapes. For example, even complex sample shapes can be decomposed into sub-shapes based on 3D images and calculations, with each sub-shape determining the portion of the sample that requires the longest time in each reagent and thus sets the overall processing time for that particular sample. Similarly, the disclosed method enables tissue processing systems with compartments for separate and simultaneous processing of batches of samples that require similar processing times in different compartments. Real-time detection of fluid wetting using this capability allows for quantification of the extent to which a sample is processed over time, aiding in the development of quantitative quality metrics that can be used to study the impact processing may have on histological tissue and downstream diagnostic assays. This capability can enable faster processing of tissue specimens, as the exact time at which the tissue finishes spreading can be determined. Even with batch-mode processing, this technique can be used to develop faster, more consistent, and standardized processing protocols for clinical tissue samples to improve process repeatability and quality assurance.Although not shown in the above example, if xylene and paraffin have different sound velocities, this technique can be extended to monitor paraffin embedding, allowing the entire fixation and processing of histological specimens to be quantitatively tracked and studied. In some embodiments, the disclosed systems and methods may be used to optimize decalcification of bone sites, which may prove useful because insufficiently decalcified samples are not suitable for IHC and overexposure to decalcifying agents can damage tissue morphology, nuclear chromatin, and nucleic acids.

[0162]

[0228] Furthermore, although all of the described tissue processing TOF studies are described in terms of decay time and amplitude, TOF monitoring may be based on and use other measures of the extent of reagent diffusion into the sample. For example, diffusivity, reagent concentration in the center of the tissue, or any other TOF-derived measure of the extent of reagent diffusion into the sample can be used to provide time-to-completion information.

[0163]

[0229] Furthermore, TOF technology can be used to determine correlations between exposure to processing reagents and the quality of histological specimens and / or the results of downstream assays. For example, correlations between over- and under-processed samples and microscopy artifacts resulting from histological tissue that is too soft or too friable can be determined. Using this information, best practices and tissue processing standards can be developed to ideally process samples so that artifacts can be prevented. Furthermore, quantitative studies to understand the impact processing can have on staining or antigen retrieval may be used to elucidate why, and to what extent, inadequately fixed samples are more susceptible to artifacts resulting from exposure to processing reagents.

[0164]

[0230] IV. Further Embodiments

[0231] In another embodiment, a method of subjecting a first sample of a particular type, size, and shape to TOF analysis to determine the time at which optimal diffusion of approximately 70% ethanol can be achieved. The method also includes a step of subjecting a second sample having approximately the same shape as the first sample to a processing protocol based on an empirically determined time for approximately 70% ethanol and the calculated times for other steps in tissue processing. For example, adjacent cores from a tumor sample can be obtained, one examined by TOF analysis to determine the optimal time for the diffusion of approximately 70% ethanol, and the second can then undergo processing based on the empirically determined protocol for injection of approximately 70% ethanol and corresponding calculated protocols for other reagents, such as approximately 90% ethanol, approximately 100% ethanol, xylene, and paraffin.

[0165]

[0232] In yet another embodiment, a tissue processing system is disclosed, comprising a controller (e.g., a microprocessor) that stores (or is retrievable from external storage) a database of protocol instructions including tissue processing steps and times; a user interface that provides user-selectable protocols corresponding to tissue samples of a particular type, shape, and size, or groups of tissue samples of a particular type, size, and size that share a particular optimized processing protocol; and a user interface that provides user-selectable protocols corresponding to each of the tissue samples of a particular type, shape, and size, such that when a user selects a sample of a particular type, shape, and size, the controller controls the tissue processing system to process the tissue according to the selected protocol. In certain embodiments, the disclosed tissue processing system comprises multiple chambers that can process different groups of tissue samples according to different user-selectable protocols corresponding to tissue samples of a particular type, size, and shape, or groups of tissue samples of a particular type, size, and shape that share a particular optimized processing protocol. In another specific embodiment, the system can include a first portion of an instrument comprising a TOF measurement chamber, and a second portion of the instrument comprising a tissue processor, wherein empirical TOF results obtained in the first portion for a first sample having a particular type, size, and shape are used to automatically select (or be displayed and selected by a user) a tissue processing protocol for a sample of the corresponding type, size, and shape, or group of samples of the type, size, and shape, that the first sample is representative of. Alternatively, the disclosed tissue processing system can include the hardware necessary for TOF monitoring in one or more chambers, thereby enabling real-time monitoring of the process and halting a particular processing step when a predetermined measure of the amount or concentration of a particular reagent is achieved in a preselected portion of the sample.

[0166]

[0233] In another embodiment, a method for determining the concentration of a reagent at a specific point within a sample immersed in the reagent at a given time is provided, the method comprising: simulating the spatial dependence of diffusion into the sample over multiple time points and for multiple candidate diffusion constants to generate a model time-of-flight; and comparing the model time-of-flight with an empirical time-of-flight to obtain an error function, where a minimum of the error function provides the diffusion constant for the sample. The method further comprises providing multiple candidate tissue porosities and generating a model time-of-flight using the diffusion constants; and comparing the model time-of-flight with the empirical time-of-flight to obtain a second error function, where a minimum of the error function provides the porosity of the sample. From the diffusion constant and porosity of the sample, the concentration at one or more specific points within the sample at a specific time can be calculated.

[0167]

[0234] In some embodiments, by comparing empirical results for determined reagent concentrations achieved at the center of a tissue sample using the above method, it is possible to generate tissue processing protocols optimized for tissue samples of different sizes, shapes, and types. Thus, in another embodiment, a modular tissue processor includes separate chambers for processing samples together that have similar optimized processing protocols. For example, one chamber may be used to process samples with similar optimized processing protocols. It is possible for a member to contain tissue samples of similar size and shape exhibiting similar porosity, or to contain a mixture of samples with different porosities but different sizes. The advantage of such a tissue processor is that tissue processing time is no longer dictated by the sample requiring the longest processing procedure, as is current practice, but rather can speed up the process for a group of samples (often one of the longest steps is between surgical removal of the tissue and the patient outcome) and ensure that samples that do not require such long processing times in various processing reagents are not overly exposed to and thereby damaged by such reagents.

[0168]

[0235] The present disclosure also provides a system comprising an acoustic monitoring device that detects acoustic waves traveled through a tissue sample and a computing device communicatively coupled to the acoustic monitoring device, the computing device configured to evaluate the speed of the acoustic waves based on the time of flight, and including instructions that, when executed, cause a processing system to perform operations including setting a range of candidate diffusion constants for the tissue sample, simulating the spatial dependence of a reagent in the tissue sample for a plurality of time points and for a first range of candidate diffusion points, determining a modeled time of flight based on the spatial dependence, repeating the simulation of the spatial dependence for each of the plurality of diffusion constants, and determining an error between the modeled time of flight for the plurality of diffusion constants and the empirical time of flight for the tissue sample, wherein the minimum value of an error function based on this error results in a diffusion constant for the tissue sample. The system further includes instructions that, when executed, cause the processing system to perform operations including: setting a range of candidate porosities for a tissue sample including a plurality of candidate porosities (such as between about 0.05 and about 0.50, e.g., between about 0.05 and about 0.40, or between about 0.05 and about 0.30); determining a second modeled time-of-flight based on the diffusion constant of the sample and the first plurality of candidate porosities; and determining a second error between the empirical time-of-flight and the second modeled time-of-flight; repeating the process of determining the second modeled time-of-flight and corresponding second error for other plurality of candidate porosities, wherein the smallest value of the error identifies the porosity of the sample. In a more specific embodiment, the system further includes instructions that, when executed, provide a spatial concentration distribution of the reagent within the sample at a specific time. In an even more 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 still further more particular embodiments, such reagent concentrations can be utilized to terminate injection of the sample with the reagent when a predetermined concentration is reached at a particular point or region within the sample, such as the center of the sample.

[0169]

[0236] The present subject disclosure applies to both biological and non-biological content, providing the ability to follow the diffusion of any substance based on its acoustic TOF curve. While the above-described operations provide for fitting the TOF curve to a single exponential function, depending on the context, a sum of Bessel functions, a double exponential function, or a quadratic function may be more appropriate. Thus, the equation itself may vary, while the novel features disclosed herein can maintain the spirit and scope of the invention when read by one of ordinary skill in the art.

[0170]

[0237] Diffusion measurements and calculations of reagent concentrations are known to be useful in many applications, including compositional analysis. It is contemplated that the present system and method may be used in any system that utilizes diffusion measurements and measurements of reagent concentrations. In one particular embodiment, the present system and method finds application in the field of monitoring the diffusion of fluids into porous materials.

[0238] In some embodiments, the porous material is a tissue sample. In many common tissue analysis methods, the tissue sample is dispersed in a fluid solution. For example, Hine (Stain Technol. 1981 Mar;56(2):119-23) discloses a method for staining the entire tissue block by immersing the tissue sample in a hematoxylin and eosin solution after fixation or before embedding and sectioning. Furthermore, fixation is often performed by immersing the unfixed tissue sample in a volume of fixative solution, and the fixative solution The fluid is allowed to diffuse into the tissue sample. As shown by Chafin et al. (PLoS ONE 8(1):e54138.doi:10.1371 / journal.pone.0054138 (2013)), failing to ensure that the fixative has sufficiently diffused into the tissue can compromise the integrity of the tissue sample. Thus, in one embodiment, the present system and method are applied to determine the sufficient diffusion time of the fixative into the tissue sample. In such a method, the user selects a minimum fixative concentration to be achieved at a specific point within the tissue sample (such as the center of the tissue sample thickness). Knowing at least the tissue thickness, the tissue geometry, and the calculated true diffusion rate, the minimum time to reach the minimum relative (relative to the surrounding fluid) fixative concentration at the center of the tissue sample can be determined. Thus, the fixative is allowed to diffuse into the tissue sample for at least a minimum time. However, to extend this to a method that can be used for real-time monitoring, the determination of tissue sample porosity disclosed herein allows for the determination of the actual fixative concentration required to achieve ensuring sample integrity. Thus, based on the systems and methods disclosed herein, other techniques such as radiolabeled tracing, mid-infrared assessment, and MRI may be used to determine the appropriate time for a particular treatment with a particular reagent, such as a fixative.

[0171]

[0239] In some embodiments, the disclosed systems and methods are used to perform a two-temperature immersion fixation method on a tissue sample. As used herein, a "two-temperature fixation method" refers to a fixation method in which the tissue is first immersed in a low-temperature fixative solution for a first period of time, followed by heating the tissue for a second period of time. The low-temperature step allows the fixative solution to diffuse throughout the tissue without substantially causing cross-linking. Then, once the tissue has sufficiently diffused throughout the tissue, a heating step results in cross-linking by the fixative. In some embodiments, the combination of low-temperature diffusion followed by a heating step results in a tissue sample that is more completely fixed than using standard methods. In some embodiments, the tissue sample is fixed by (1) immersing an unfixed tissue sample in a low-temperature 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 increase after a threshold TOF is measured (fixation step). In some embodiments, the diffusion step is carried out in a fixative solution that is below 20°C, below 15°C, below 12°C, below 10°C, about 0°C to about 10°C, about 0°C to about 12°C, about 0°C to about 15°C, about 2°C to about 10°C, about 2°C to about 12°C, about 2°C to about 15°C, about 5°C to about 10°C, about 5°C to about 12°C, or about 5°C to about 15°C. In exemplary embodiments, the environment surrounding the tissue sample is allowed to rise within a range of about 20°C to about 55°C during the fixation step. In some embodiments, the fixative is an aldehyde-based cross-linking fixative, such as a glutaraldehyde- and / or formalin-based solution. Examples of aldehydes often used in immersion fixation are listed below.

[0172]

[0240] Formaldehyde (for most tissues, the standard working concentration is about 5 to about 10% formalin, although concentrations as high as about 20% formalin have been used for some tissues); glyoxal (standard working concentration 17 to 86 mM); and glutaraldehyde (standard working concentration 200 mM).

[0173]

[0241] Aldehydes are often used in combination with one another. A standard aldehyde combination includes 10% formalin + 1% (w / v) glutaraldehyde. Typical aldehydes used for specialized fixation applications include fumaraldehyde, 12.5% hydroxyadipaldehyde (pH 7.5), 10% crotonaldehyde (pH 7.4), 5% pyruvaldehyde (pH 5.5), and 10% acetaldehyde (pH 7.5). , 10% acrolein (pH 7.6), and 5% methacrolein (pH 7.6). Other specific examples of aldehyde-based fixative solutions used for immunohistochemistry are listed in Table 2.

[0174] [Table 2]

[0175]

[0242] In some embodiments, the fixative solution is selected from Table 2. In some embodiments, the aldehyde concentration used is higher than the standard concentrations described above. For example, a high-concentration aldehyde-based fixative solution can be used that has an aldehyde concentration at least 1.25-fold higher than the standard concentration used to fix tissues selected for immunohistochemistry having approximately the same composition. In some examples, the high-concentration aldehyde-based fixative solution is selected from greater than 20% formalin, about 25% or more formalin, about 27.5% or more formalin, about 30% or more formalin, about 25% to about 50% formalin, about 27.5% to about 50% formalin, 30% to about 50% formalin, about 25% to about 40% formalin, about 27.5% to about 40% formalin, and about 30% to about 40% formalin. When used in this context, the term "about" refers to the concentration of formalin used in Bauer et al., Dynamic Subnanosecond Time-of-Flight Detection for Immunohistochemistry. It is intended to include concentrations that do not result in a statistically significant difference in diffusion at 4°C as measured by Ultra-precise Diffusion Monitoring and Optimization of Biomarker Preservation, Proceedings of SPIE, Vol. 9040, 90400B-1 (March 20, 2014).

[0176]

[0243] In some embodiments, the two-temperature fixation process is particularly useful for methods of detecting certain labile biomarker compounds in tissue samples containing, for example, phosphorylated proteins, DNA, and RNA molecules (such as miRNA and mRNA). See PCT / EP2012 / 052800 (incorporated herein by reference). In some embodiments, fixed tissue samples obtained using these methods contain the labile biomarkers of such biomarkers. The labile label can be analyzed for its presence. Thus, in some embodiments, methods for detecting a labile label in a sample are provided, comprising fixing tissue according to the two-temperature fixation method disclosed herein and contacting the fixed tissue sample with an analyte-binding entity capable of specifically binding to a labile label, such as FOXP3. Examples of analyte-binding entities include antibodies and antibody fragments (including single-chain antibodies) that bind to target antigens, T cell receptors (including single-chain receptors) that bind to MHC:antigen complexes, MHC:peptide multimers (which bind to specific T cell receptors), aptamers that bind to specific nucleic acid or peptide targets, zinc fingers that bind to specific nucleic acids, peptides, and other molecules, receptor complexes (including single-chain receptors and chimeric receptors) that bind receptor ligands, receptor ligands that bind receptor complexes, and nucleic acid probes that hybridize to specific nucleic acids. For example, immunohistochemical methods for detecting phosphorylated proteins in a tissue sample are provided, comprising contacting fixed tissue obtained according to the two-temperature fixation method described above with an antibody specific to the phosphorylated protein and detecting binding of the antibody to the phosphorylated protein. In some embodiments, an in situ hybridization method for detecting nucleic acid molecules is provided, the method comprising contacting fixed tissue obtained according to the two-temperature fixation method described above with a nucleic acid probe specific for the nucleic acid of interest and detecting binding of the probe to the nucleic acid of interest.

[0177]

[0244] The foregoing disclosure of exemplary embodiments of the present subject disclosure has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the present subject disclosure to the precise form disclosed. Many variations and modifications of the embodiments described herein will be apparent to those skilled in the art in light of the above disclosure. The scope of the present subject disclosure is defined only by the claims appended hereto, and by equivalents thereof.

[0178]

[0245] Furthermore, in described exemplary embodiments of the present subject disclosure, the specification may present the methods and / or processes of the present subject disclosure as a particular sequence of steps. However, insofar as the method or process does not rely on the particular order of steps described herein, the method or process should not be limited to the particular order of steps described. Those skilled in the art will understand that other orders of steps may be possible. Thus, the particular order of steps described herein should not be considered a limitation on the scope of the claims. Additionally, claims directed to the methods and / or processes of the present subject disclosure should not be limited to performing the steps in the order described; those skilled in the art will readily understand that the order may be changed and still remain within the spirit and scope of the present subject disclosure.

[0179] References 1. Dapson, RW (2007) Macromolecular changes caused by formalin fixation and antigen retrieval. Biotech Histochem 82,133~140 2. Rastogi, V., Puri, N., Arora, S., Kaur, G., Yadav, L., and Sharma, R. (2013) Artefacts: a diagnostic dilemma-a review. J Clin Diagn Res 7,2408~2413 3. Bancroft, JD, Suvarna, KS, and Layton, C. (2012) Bancroft's Theory and Practice of Histological Techniques, Churchill Livingstone. 4. Farmer, NJ, Hall, JB1., and Rolls, GO (2008) Artifacts in Histological and Cytological Preparations, Leica Microsystem. s 5. Leu,F.J.,Chen,C.F.,and Sun,A.M.(1993)A new method of tissue processing that causes no shrinkage or distortion.Lab Invest 69,121~130 6. Iwadare,T.,Mori,H.,Ishiguro,K.,and Takeishi,M.(1984)Dimensional changes of tissues in the course of processing.J Micros c 136,323~327 7. Otali,D.,He,Q.,Stockard,C.R.,and Grizzle,W.E.(2013)Preservation of immunorecognition by transferring cells from 10% neutral buffered formalin to 70% ethanol.Biotech Histochem 88,170~180 8. Chafin,D.,Theiss,A.,Roberts,E.,Borlee,G.,Otter,M.,and Baird,G.S.(2013)Rapid two-temperature formalin fixation. PLoS One 8,e54138 9. Hewitt,S.M.,Lewis,F.A.,Cao,Y.,Conrad,R.C,Cronin,M.,Danenberg,K.D.,Goralski,T.J.,Langmore,J.P.,Raja,R.G.,Williams,P.M.,Palma,J.F.,and Warrington, J. A.(2008)Tissue handling and specimen preparation in surgical pathology: issues concerning the recovery of nucleic acids from formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med 132,1929~1935 10. Theiss,A.P.,Chafin,D.,Bauer,D.R.,Grogan,T.M.,and Baird,G.S.(2014)Immunohistochemistry of colorectal cancer biomarker phosphorylation requires controlled tissue fixation. PLoS One 9,e113608 11. Werner,M.,Chott,A.,Fabiano,A.,and Battifora,H.(2000)Effect of formalin tissue fixation and processing on immunohistochemistry. Am J Surg Pathol 24,1016~101912. Engel,K.B.,and Moore,H.M.(2011)Effectsof preanalytical variables on the detection of proteins by immunohistochemistry in formalin- fixed, paraffin-embedded tissue. Arch Pathol Lab Med 135,537~543 13. Otali,D.(2007)The combined effect of formalin fixation and individual steps in tissue processing on immunorecognition. Master of Science,The University of Alabama at Birmingham 14. Otali,D.,Stockard,C.R.,Oelschlager,D.K.,Wan,W.,Manne,U.,Watts,S.A.,and Grizzle,W.E.(2009)Combined effects of formalin fixation and tissue processing on immu norecognition. Biotech Histochem 84,223~247 15. Williams,J.H.,Mepham,B.L., andWright,D.H.(1997)Tissue preparation for immunocytochemistry.J Clin Pathol 50,422~428 16. Thompson,S.M.,Craven,R.A.,Nirmalan,N.J.,Harnden,P.,Selby,P.J.,and Banks,R.E.(2013)Impact of pre-analytical factors on the proteomic analysis of formalin-fixed paraffin-embedded tissue. Proteomics Clin Appl 7,241~251 17. Bass,B.P.,Engel,.B.,Greytak,S.R.,and Moore,H.M.(2014)A review of preanalytical factors affecting molecular, protein, and morphological analysis of formalin-fixed, paraffin-embedded (FFPE) tissue: how well do you know your FFPE specimen? Arch Pathol Lab Med 138,1520~1530 18. Chung,J.Y.,Braunschweig,T.,Williams,R.,Guerrero,N.,Hoffmann,K.M.,Kwon,M.,Song,Y.K.,Libutti,S.K.,and Hewitt,S.M.(2008)Factors in Tissue Handling and Processing That Impact RNA Obtained From Formalin-fixed, Paraffin-embedded Tissue, in J Histochem Cytochem. pp1033~1042 19. Xie,R.,Chung,J.Y.,Ylaya,K.,Williams,R.L.,Guerrero,N.,Nakatsuka,N.,Badie,C,and Hewitt,S.M.(2011)Factors influencing the degradation of archival formalin-fixed paraffin-embedded tissue sections. J Histochem Cytochem 59,356~365 20. von Ahlfen,S.,Missel,A.,Bendrat,K.,and Schlumpberger,M.(2007)Determinants of RNA Quality from FFPE Samples, in PLoS ONE.pp 21. O’Leary,T.I,Fowler,C.B.,Evers,D.L.,and Mason,J.T.(2009)Protein fixation and antigen retrieval: chemical studies. Biotech Histochem 84,217~221 22. Fowler,C.B.,O’Leary,T.J.,and Mason,J.T.(2008)Modeling formalin fixation and histological processing with ribonuclease A: effects of ethanol dehydration on reversal of formaldehyde cross-links. Lab Invest 88,785~791 23. Shi,S.,andTaylor,C.R.(2011)Antigen Retrieval Immunohistochemistry Based Research and Diagnostics, 24. Bauer,D.R.,Stevens,B.,Taft,J.,Chafin,D.,Petre,V.,Theiss,A.P.,and Otter,M.(20 14)Dynamic subnanosecond time-of-flight detection for ultra-precise diffusion monitoring and optimization of biomarker preservation, in SPIE Medical Imaging, San Diego 25. Bauer,D.R.,Steven,B.,Chafin,D.,Theiss,A.P.,and Otter,M.(2016)Active monitoring of formaldehyde diffusion into histological tissues with digital acoustic interferometry. Journal of Medical Imaging 3,017002~17002 26. (2016) speed of sound in common materials for use with ultrasonic flow meters. 27. (2016) Speed of Sound in some common Liquids. 28. Alers,J.C,Krijtenburg,P.J.,Vissers,K.J.,and van Dekken,H.(1999)Effect of bone decalcification procedures on DNA in situ hybridization and comparative genomic hybridization. EDTA is highly preferable to a routinely used acid decalcifier. J Histochem Cytochem 47,703~710 29. Neumeister,V.M.(2014)Tools to assess tissue quality. Clin Biochem 47,280~287

Claims

1. 1. A method for processing a tissue sample, comprising: For each of a plurality of candidate diffusion constants, simulating the concentration of the reagent at different locations within the tissue sample and at a plurality of different time points after the tissue sample is immersed in the reagent; determining a simulated time of flight (TOF) of an acoustic wave passing through the tissue sample based on the simulated concentration of the reagent within the tissue sample for each of the plurality of candidate diffusion constants and for a first candidate porosity representing a volume fraction of the tissue sample available for fluid exchange with the reagent; obtaining an empirical TOF of an acoustic wave passing through the tissue sample; determining an error function between the simulated TOF and the empirical TOF for each of the plurality of candidate diffusion constants; determining a minimum error function among said error functions; designating the candidate diffusion constant associated with the smallest error function among the plurality of candidate diffusion constants as the true diffusion constant of the tissue sample; determining a second simulated TOF of an acoustic wave passing through the tissue sample for the true diffusion constant and for each of a plurality of candidate porosities different from the first candidate porosity; determining a second error function between the second simulated TOF and the empirical TOF for each of the plurality of candidate porosities; determining a minimum second error function among the second error functions; designating the candidate porosity associated with the smallest second error function among the plurality of candidate porosities as the true porosity of the tissue sample; A method comprising:

2. 2. The method of claim 1, wherein simulating the concentration of the reagent for each of the plurality of candidate diffusion constants comprises determining a solution of a heat diffusion equation applied to the tissue sample for each of the plurality of candidate diffusion constants, the solution representing the simulated concentration of the reagent for each of the plurality of candidate diffusion constants.

3. The method of claim 1 or 2, wherein the tissue sample is of a tissue type and has a shape and a size.

4. The method of claim 3 , wherein the shape comprises a cylindrical shape, a cubic shape, or a box shape.

5. determining an immersion time for the tissue sample that will result in a desired diffusivity and / or a desired concentration of the reagent at a particular location within the tissue sample based at least on the true diffusion constant or the true diffusion constant and the true porosity; immersing a second tissue sample of substantially similar type, shape, and / or size to the tissue sample for the immersion time of the tissue sample; The method of claim 1 further comprising:

6. 1. A tissue processing system comprising: an acoustic monitoring device configured to acquire an empirical time of flight (TOF) of an acoustic wave passing through a tissue sample immersed in a reagent; 1. A processor, comprising: For each of a plurality of candidate diffusion constants, simulating the concentration of the reagent at different locations within the tissue sample and at a plurality of different time points after the tissue sample is immersed in the reagent; determining a simulated TOF of an acoustic wave passing through the tissue sample based on the simulated concentration of the reagent within the tissue sample for each of the plurality of candidate diffusion constants and for a first candidate porosity representing a volume fraction of the tissue sample available for fluid exchange with the reagent; determining an error function between the simulated TOF and the empirical TOF for each of the plurality of candidate diffusion constants; determining a minimum error function among said error functions; designating the candidate diffusion constant associated with the smallest error function among the plurality of candidate diffusion constants as the true diffusion constant for the tissue sample; determining a second simulated TOF of an acoustic wave passing through the tissue sample for the true diffusion constant and for each of a plurality of candidate porosities different from the first candidate porosity; determining a second error function between the second simulated TOF and the empirical TOF for each of the plurality of candidate porosities; determining a minimum second error function among the second error functions; designating the candidate porosity associated with the smallest second error function among the plurality of candidate porosities as the true porosity of the tissue sample. a processor configured to execute program instructions for: A tissue processing system comprising:

7. 7. The system of claim 6, wherein the processor is further configured to execute program instructions for simulating a concentration of the reagent for each of the plurality of candidate diffusion constants by determining a solution of a heat diffusion equation applied to the tissue sample for each of the plurality of candidate diffusion constants, the solution representing the simulated concentration of the reagent for each of the plurality of candidate diffusion constants.

8. The system of claim 6 or 7, wherein the tissue sample is of a tissue type and has a shape and a size.

9. The system of claim 8 , wherein the shape comprises a cylindrical shape, a cubic shape, or a box shape.

10. The processor: determining an immersion time for the tissue sample that will result in a desired diffusivity and / or a desired concentration of the reagent at a particular location within the tissue sample based at least on the true diffusion constant or the true diffusion constant and the true porosity; For a second tissue sample of substantially similar type, shape, and / or size to the tissue sample, specify an immersion time for the second tissue sample in the reagent equal to the determined immersion time for the tissue sample. The system of claim 6 further configured to execute program instructions for:

11. The processor: monitoring the time the second tissue sample is immersed in the reagent; When the time equals the determined immersion time, alerting a user to remove the second tissue sample from immersion in the reagent or causing the system to automatically remove the second tissue sample from immersion in the reagent. The system of claim 10 further configured to execute program instructions for:

12. 12. The system of claim 11, wherein the reagent comprises any one of about 70% ethanol, about 90% ethanol, about 100% ethanol, about 100% xylene, and about 100% paraffin.

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