Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Disclosed herein are novel quinone methide analog precursors and embodiments of a method and a kit of using the same for detecting one or more targets in a biological sample. The method of detection comprises contacting the sample with a detection probe, then contacting the sample with a labeling conjugate that comprises an enzyme. The enzyme interacts with a quinone methide analog precursor comprising a detectable label, forming a reactive quinone methide analog, which binds to the biological sample proximally to or directly on the target. The detectable label is then detected. In some embodiments, multiple targets can be detected by multiple quinone methide analog precursors interacting with different enzymes without the need for an enzyme deactivation step.

Abstract: Disclosed herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.

Abstract: Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.

Abstract: The present disclosure provides a method of separating cellular particles from a tissue sample and then sorting the cellular particles into two or more cellular particle populations.

Abstract: Techniques are provided for stitching tile images. A local registration can be performed to determine spatial relationships between pairs of adjacent tile images, in which the spatial relationships may be determined using normalized cross correlation (NCC) scores computed within a multi-resolution framework. A global placement can be performed to position all scanned tile images relative to one another. The global placement is determined with weighted least squares utilizing the determined spatial relationships between all adjacent greyscale tile images and the NCC scores as weights.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: Disclosed herein are systems and methods of calibrating a microscope or an imaging system prior to acquiring image data of a sample. In some embodiments, a method is disclosed including the steps of (a) running a power output calibration module to calibrate an imaging apparatus for repeatability; (b) running an image intensity calibration module to calibrate the imaging apparatus for reproducibility and to mitigate differences in detection efficiency between channels; (c) collecting image data from a microscope or imaging system; (d) optionally running an unmixing module to unmix the collected image data into individual image channel images; and (e) optionally running a contrast agent intensity correction module to calibrate for differences in brightness between different contrast agents.

Abstract: The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.

Abstract: Disclosed herein are antibody-nanoparticle conjugates that include two or more nanoparticles (such as gold, palladium, platinum, silver, copper, nickel, cobalt, iridium, or an alloy of two or more thereof) directly linked to an antibody or fragment thereof through a metal-thiol bond. Methods of making the antibody-nanoparticle conjugates disclosed herein include reacting an arylphosphine-nanoparticle composite with a reduced antibody to produce an antibody-nanoparticle conjugate. Also disclosed herein are methods for detecting a target molecule in a sample that include using an antibody-nanoparticle conjugate (such as the antibody-nanoparticle conjugates described herein) and kits for detecting target molecules utilizing the methods disclosed herein.

Abstract: Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.

Abstract: Antibodies, compositions, systems, and methods for detecting C4.4a, for example immunohistochemistry methods for detecting C4.4a using a C4.4a antibody. The antibody may be obtained by immunizing a host with a C4.4a protein such as a peptide downstream of the signal peptide. The antibodies may be adapted to detect the uPAR-like domain 1 and uPAR-like domain 2. Also featured are methods for diagnosing C4.4a-associated tumors using C4.4a antibodies disclosed herein.

Abstract: Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.

Abstract: Disclosed herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Abstract: The present disclosure is directed to opposables including a body having a plurality of cavities disposed therein. Each cavity can be designed to contain one or more reagents, liquids, or fluids which may be applied to a specimen-bearing surface. In some embodiments, the cavities include one or more reagent chambers, the reagent chambers can have one or more seals such that the reagents, liquids, or fluids contained therein may be stored and released to the specimen-bearing surface.

Abstract: Methods, systems, and compositions featuring a solid, dissolvable reagent composition for delivering the reagent, such as an antibody, probe, chromogen, etc., to a sample. The present invention also features methods of producing said compositions, and automated systems featuring the use of the solid, dissolvable reagent compositions. The solid, dissolvable reagent composition may comprise a water-soluble polymer film, such as a polyvinyl alcohol film, infused with the reagent, wherein when applied to the sample, the water-soluble polymer film with reagent contacts the sample (e.g., tissue) and dissolves.

Abstract: A method and system for classifying field of view (FOV) images of histological slides into various categories that include certain stain patterns, artifacts, and/or other features of interest are provided herein. Few-shot learning (e.g., a prototypical network) techniques are used to train a deep convolutional neural network using a small number of training samples for a small number of image classes for classifying stain images belonging to a larger number of image classes.

Abstract: Automated systems and methods for training a multilayer neural network to classify cells and regions from a set of training images are presented. Automated systems and methods for using a trained multilayer neural network to classify cells within an unlabeled image are also presented.

Abstract: A multiplex image is accessed that depicts a particular slice of a particular sample stained with two or more dyes. Using a Generator network, a predicted singleplex image is generated that depicts the particular slice of the particular sample stained with each of the expressing biomarkers. The Generator network may have been trained by training a machine-learning model using a set of training multiplex images and a set of training singleplex images. Each of the set of training multiplex images depicted a slice of a sample stained with two or more dyes. Each of the set of training singleplex images depicted a slice of a sample stained with a single dye. The machine-learning model included a Discriminator network configured to discriminate whether a given image was generated by the Generator network or was a singleplex image of a real slide. The method further includes outputs the predicted singleplex image.