Abstract: The disclosure provides for methods, compositions, systems, devices, and kits for determining the number of distinct targets in distinct spatial locations within a sample. In some examples, the methods include: stochastically barcoding the plurality of targets in the sample using a plurality of stochastic barcodes, wherein each of the plurality of stochastic barcodes comprises a spatial label and a molecular label; estimating the number of each of the plurality of targets using the molecular label; and identifying the spatial location of each of the plurality of targets using the spatial label. The method can be multiplexed.

Abstract: Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Abstract: The disclosure provides for methods, compositions, and kits for multiplex nucleic acid analysis of single cells. The methods, compositions and systems may be used for massively parallel single cell sequencing. The methods, compositions and systems may be used to analyze thousands of cells concurrently. The thousands of cells may comprise a mixed population of cells (e.g., cells of different types or subtypes, different sizes).

Abstract: The disclosure provides for methods, compositions, and kits for multiplex nucleic acid analysis of single cells. The methods, compositions and systems may be used for massively parallel single cell sequencing. The methods, compositions and systems may be used to analyze thousands of cells concurrently. The thousands of cells may comprise a mixed population of cells (e.g., cells of different types or subtypes, different sizes).

Abstract: Methods, systems and platforms for digital imaging of multiple regions of an array, and detection and counting of the labeled features thereon, are described.

Abstract: The disclosure provides for methods, compositions, and kits for multiplex nucleic acid analysis of single cells. The methods, compositions and systems may be used for massively parallel single cell sequencing. The methods, compositions and systems may be used to analyze thousands of cells concurrently. The thousands of cells may comprise a mixed population of cells (e.g., cells of different types or subtypes, different sizes).

Abstract: The disclosure provides for methods, compositions, and kits for multiplex nucleic acid analysis of single cells. The methods, compositions and systems may be used for massively parallel single cell sequencing. The methods, compositions and systems may be used to analyze thousands of cells concurrently. The thousands of cells may comprise a mixed population of cells (e.g., cells of different types or subtypes, different sizes).

Abstract: Compositions, methods and kits are disclosed for high-sensitivity counting of individual molecules by stochastic labeling of a identical molecules in mixtures of molecules by attachment of a unique label-tags from a diverse pool of label tags to confer uniqueness to otherwise identical or indistinguishable events. Individual occurrences of target molecules randomly choose from a non-depleting reservoir of diverse label-tags. Labeled molecules may be detected by hybridization or sequencing based methods. Molecules that would otherwise be identical in information content are labeled to create a separately detectable product that can be distinctly detected. The disclosed stochastic transformation methods reduce the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed label-tags are present. The methods may be used, for example, to count a given species of molecule within a sample.

Abstract: Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Abstract: Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Abstract: Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Abstract: Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.