![]() In addition, tissue sections can provide only static images, necessitating the use of indirect methods such as pseudo-time analysis to infer cellular trajectories over time 13, 14, and to our knowledge none of the currently available spatial transcriptomics technologies has been combined with in vivo imaging. For example, 3D expression profiles and cell segmentation have been demonstrated in tissue sections 11, 12 but the section thickness is limited by messenger RNA probe diffusion. They map the gene expression profiles onto two-dimensional (2D) images, and extrapolation to three-dimensional (3D) architecture of intact tissues is still limited. However, apart from Niche-seq 7, they have been applied only in vitro or they have relied on the generation of tissue sections 3, 4, 5, 8, 10, which confines their applicability to tissues that are easily sectioned. In contrast to single-cell RNA sequencing (scRNA-seq), which provides deep insights into heterogeneities within cell populations but does not preserve the spatial relationships between individual cells, techniques such as MERFISH 9, seqFISH 4 and Slide-seq 8 can link these heterogeneities to differences in spatial composition and cellular proximity. Spatial transcriptomics is a rapidly advancing field that encompasses a range of different technologies capable of spatially resolved gene expression analysis 1, 2, 3, 4, 5, 6, 7, 8, 9. Furthermore, the ability of Image-seq to isolate viable, intact cells should make it compatible with a range of downstream single-cell analysis tools including multi-omics protocols. We discovered that DPP4 is a highly upregulated gene during early progression of acute myeloid leukemia and that it marks a more proliferative subpopulation that is confined to specific bone marrow microenvironments. We have used both high-throughput, droplet-based sequencing as well as SMARTseq-v4 library preparation to demonstrate its application to bone marrow and leukemia biology. The technique therefore combines spatial information with highly sensitive RNA sequencing readouts from individual, intact cells. Cell samples are isolated from intact tissue and processed with state-of-the-art library preparation protocols. ![]() It is compatible with in situ and in vivo imaging and can document the temporal and dynamic history of the cells being analyzed. Here, we present Image-seq, a technology that provides single-cell transcriptional data on cells that are isolated from specific spatial locations under image guidance, thus preserving the spatial information of the target cells. While the properties of individual cells are increasingly being deciphered using powerful single-cell sequencing technologies, understanding their spatial organization and temporal evolution remains a major challenge. This service can be performed in conjunction with the 5′ Next GEM library prep or the VDJ immune profiling (B cell or T cell) library prep, but is not a stand alone service.Tissue function depends on cellular organization. It can also be used to multiplex more than one sample into a single library prep. This technology can also determine antigen specificity of single T cells with Feature Barcode peptide-MHC multimers to study the dynamic interactions between lymphocytes and antigens. Use this technique to measure both gene and cell surface protein expression in the same cell to identify protein isoforms, detect protein for low abundance transcripts, and further increase phenotypic specificity. Libraries are generated and sequenced and 10x Barcodes are used to associate individual reads back to the individual partitions. It does so by partitioning thousands of cells into nanoliter-scale Gel Beads-in-emulsion (GEMs), where all generated cDNA share a common 10x Barcode. A pool of ~750,000 barcodes are sampled separately to index each cell’s transcriptome and cell surface protein. This is accomplished by labeling cell surface proteins with antibodies conjugated to a Feature Barcode oligonucleotide, followed by direct capture of the Feature Barcode by the Gel Bead primer. The Chromium Single Cell Feature Barcode technology offers a comprehensive, scalable approach to detect cell surface proteins along with the gene expression and immune repertoire information from the same single cell. UVA Child Development & Rehabilitation Center.Translational Health Research Institute of Virginia.Institute of Law, Psychiatry & Public Policy.Child Health Research Center (Pediatrics).Thaler Center for AIDS & Human Retrovirus Research Center for Immunity, Inflammation & Regenerative Medicine.Center for Behavioral Health & Technology.Molecular Physiology & Biological Physics.Microbiology, Immunology, & Cancer Biology (MIC).
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