Abstract: Provided herein are methods for capturing a connected probe and/or a capture handle sequence to a capture domain of a capture probe.

Abstract: Provided herein are methods for capturing a connected probe and/or a capture handle sequence to a capture domain of a capture probe.

Abstract: A sample holder includes a first member featuring a first retaining mechanism configured to retain a first substrate that includes a sample, a second member featuring a second retaining mechanism configured to retain a second substrate that includes a reagent medium, and an alignment mechanism connected to at least one of the first and second members, and configured to align the first and second members such that the sample contacts at least a portion of the reagent medium when the first and second members are aligned.

Abstract: A sample holder is provided. The sample holder includes a first member including a first retaining mechanism configured to retain a first substrate comprising a sample. The first retaining mechanism configured to retain the first substrate disposed in a first plane. The sample holder further includes a second member including a second retaining mechanism configured to retain a second substrate including a reagent medium disposed in a second plane. The sample holder further includes an alignment mechanism connected to one or both of the first member and the second member. The alignment mechanism configured to align the first and second members along the first plane and/or the second plane such that the sample contacts at least a portion of the reagent medium when the first and second members are aligned and within a threshold distance along an axis orthogonal to the second plane.

Abstract: Provided herein are methods for capturing a connected probe and/or a capture handle sequence to a capture domain of a capture probe.

Abstract: Provided herein are methods, compositions, and systems for improved in situ detection of analytes and spatial analysis using, e.g., a sequencing readout.

Abstract: Provided herein are methods for capturing an exogenous probe and/or a capture handle sequence to a capture domain of a capture probe. Compositions, systems, and kits also are disclosed. In some instances, the methods include detecting an exogenous nucleic acid in a biological sample by hybridizing a first exogenous probe oligonucleotide and a second exogenous probe oligonucleotide to an exogenous nucleic acid in a biological sample on a first substrate, coupling the exogenous probe oligonucleotides, and capturing the coupled exogenous probe on a spatial array.

Abstract: Provided herein are methods for capturing a connected probe and/or a capture handle sequence to a capture domain of a capture probe.

Abstract: A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be registered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecting fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

Abstract: Provided herein are methods for capturing a connected probe and/or a capture handle sequence to a capture domain of a capture probe.

Abstract: Provided herein are methods, compositions and systems for identifying spatial gene expression of analytes from glioblastoma derived tissue. The methods discloses herein include using templated ligation probe pairs to identify location of a disease proliferating region in a glioblastoma-derived sample by detecting analytes in the region and hybridizing a ligation product comprising the probe pairs to a capture probe on a spatial array.

Abstract: The present disclosure relates to materials and methods for spatially analyzing nucleic acids that have been fragmented with a transposase enzyme, alone or in combination with other types of analytes

Abstract: The present disclosure provides electrophoretic systems, methods and compositions for spatial analysis, which can serve to magnify or demagnify spatial resolution of analytes of interest that are captured using electrophoresis. Some implementations can use a diverging or converging electric field in an electrophoretic capture system. Such a divergent or convergent electric field, as opposed to a parallel electric field, can be generated by, for example, utilizing different sizes of electrodes associated with or imbedded in substrates. Also provided herein are electrophoretic systems, methods and compositions for spatial analysis, which can serve to selectively migrate one or more analytes from a region of interest in the biological sample for capture using electrophoresis.

Abstract: Provided herein are methods of enhancing spatial resolution of an analyte using sandwich maker system. The methods and systems used herein include a first substrate that includes a plurality of probes that include a capture domain and a spatial domain and a second substrate that includes a plurality of probes comprising a capture domain and a spatial domain.

Abstract: Devices, systems, and their methods of use, for generating and collecting droplets are provided. The invention provides multiplex devices that increase droplet formation in a limited area.

Abstract: Flowcell devices configured for reversible assembly, and methods of assembly, disassembly, and use thereof are provided. The methods and devices allow the interior of the flowcell device to be accessed without damaging, or otherwise disturbing sensitive samples and surfaces.

Abstract: A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be registered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecting fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

Abstract: Provided herein are methods and systems for determining the location of one or more analytes in a biological sample with electrophoresis analyte transfer modes.

Abstract: A sample holder includes a first member featuring a first retaining mechanism configured to retain a first substrate that includes a sample, a second member featuring a second retaining mechanism configured to retain a second substrate that includes a reagent medium, and an alignment mechanism connected to at least one of the first and second members, and configured to align the first and second members such that the sample contacts at least a portion of the reagent medium when the first and second members are aligned.

Abstract: Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.