10X Genomics

The present disclosure provides compositions, methods, systems, and devices for polynucleotide processing and analyte characterization. Such polynucleotide processing may be useful for a variety of applications, including analyte characterization by polynucleotide sequencing. The compositions, methods, systems, and devices disclosed herein generally describe barcoded oligonucleotides, which can be bound to a bead, such as a gel bead, useful for characterizing one or more analytes including, for example, protein (e.g., cell surface or intracellular proteins), genomic DNA, and RNA (e.g., mRNA or CRISPR guide RNAs). Also described herein, are barcoded labelling agents and oligonucleotide molecules useful for tagging analytes for characterization.

The present disclosure relates in some aspects to methods and compositions for assessing performance of in situ analyte detection and/or platforms (e.g., methods, assays, workflows, and/or instruments for in situ analyte detection and/or sequencing). In some aspects, performance can be assessed for an individual platform, or the performance of two or more different platforms can be compared. In some aspects, assessing performance comprises a standardized in situ analyte detection workflow and analysis on a test biological sample comprising defined cell populations.

The present disclosure provides methods, compositions and systems for analyzing individual cells or cell populations through a partitioned analysis of contents of individual cells or cell populations, such as cancer cells and cells of the immune system. Individual cells or cell populations may be co-partitioned with processing reagents for accessing cellular contents, and for uniquely identifying the content of a given cell or cell population, and subsequently analyzing the content of the cell and characterizing it as having derived from an individual cell or cell population, including analysis and characterization of nucleic acid(s) from the cell through sequencing.

The present disclosure relates to compositions and methods for reversible fixation of biological samples using fixation reagents that form bis-carbamate crosslinks between amine-bearing moieties in biomolecules.

The present disclosure in some aspects relates to methods and compositions for accurately detecting and quantifying multiple analytes present in a biological sample. In some aspects, the methods and compositions provided herein address issues associated with the heterogeneity of analyte abundance (e.g., gene expression levels) and variations among reactions at different locations of a sample (e.g., amplification reaction starting earlier at one location than another location). In some aspects, a method disclosed herein provides a tighter distribution of signal spot size and intensity in a sample, as compared to methods that result in a wide and heterogeneous size and intensity distribution of signal spots.

Methods and compositions to minimize barcode exchange during the preparation of barcoded next-generation sequencing libraries prepared from a single cell. The methods utilize oligonucleotides containing a 3'-terminated blocking group or sequences that prevent amplification or extension.

In some aspects disclosed herein are methods and compositions for detecting analytes such as a plurality of nucleic acid molecules in a biological sample, said method comprising generating and analyzing a first detectable signal and a second detectable signal corresponding to a first target sequence and a second target sequence, respectively. The first and second target sequences can be present and/or expressed at different levels in the biological sample.

Systems and methods for tissue classification are provided. An image of tissue on a substrate is obtained as a plurality of pixels. Fiducial markers are on the substrate boundary. Pixels are assigned to a first class, indicating tissue sample, or a second class, indicating background. The assigning uses the fiducial markers to define a bounding box within the image and disregards pixels outside the box. Then, heuristic classifiers are applied to the pixels: for each respective pixel in the plurality of pixels, each heuristic classifier votes for the respective pixel between the first and second class, thereby forming an aggregated score for each pixel that in one of first class, likely first class, likely second class, and obvious second class. The aggregated score and intensity of each pixel is applied to a segmentation algorithm to assign a probability to each pixel of being tissue sample or background.

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