Project description:To facilitate scalable profiling of single cells, we developed Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. Over 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path towards comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.
Project description:Single-cell RNA sequencing experiments commonly use 10x Genomics kits due to their high-throughput capacity and standardized protocols. Recently, Parse Biosciences introduced an alternative technology that uses multiple in-situ barcoding rounds within standard 96-well plates. The Parse technology allows the analysis of more cells from multiple samples in a single run without using additional reagents or specialized microfluidics equipment. To assess the efficacy of both platforms, we carried out a benchmark study using biological and technical replicates of young murine thymus as a complex immune tissue. To optimally utilize the capacity of Parse high-throughput kits, we also performed sequencing of murine splenocytes from a chronic stress model and control animals.
Project description:We report a method enabling simultaneous, ultra-high throughput single-cell-barcoding, of millions of cells for targeted single cell analysis of proteins and RNAs. This method termed Quantum Barcoding (QBC) circumvents the need to isolate single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette) cell-specific codes are added to each tagged molecule within cells. This is accomplished through sequential rounds of the well-established process of classic “split-pool synthesis”. We demonstrate the utility of this currently research use only technology in multiple model systems.
Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D solid-tumour models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq simultaneously reveals both inter- and intra-cellular signalling in tumour microenvironment organoids.
Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D solid-tumour models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq simultaneously reveals both inter- and intra-cellular signalling in tumour microenvironment organoids.
Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq can simultaneously un- cover inter- and intra-cellular regulation of tumour microenvironment plasticity. This SuperSeries is composed of the SubSeries listed below.
Project description:We introduce single cell combinatorial indexed cytometry by sequencing (SCITO-seq), a single cell proteomics workflow that combines split-pool indexing and droplet-based sequencing
Project description:To establish an ultra-high-throughput single cell chromatin accessibility profiling method that is cost-effective and widely accessible, we built on sci-ATAC-seq (Cusanovich, D. A. et al. Science. 2015; Amini, S. et al. Nature Genetics. 2014) and SPLIT-seq (Rosenberg, A. B. et al. Science. 2018) to design SPATAC-seq, which in situ label chromatin fragment in the same single cell through combinatorial barcoding. Briefly, in SPATAC-seq, (1) fixed nuclei are transposed in 48 different wells by 48 unique Tn5 transposase, which containing barcoded adaptors and 5'-phosphorylation; (2) the nuclei from all wells are collected and redistributed into second and third 48-well plate in turn, where the next two rounds of indexing are achieved through into either end of the custom transposome, which result in the generation of more than 110,000 (48^3) unique barcode combinations. (3) the nuclei are pooled, split into sublibraries and lysed, and the DNA was amplified by polymerase chain reaction (PCR), which introduce illumina sequencing barcodes and complete libraries construction. (4) After sequencing, fastq files were demultiplexed according to the same four-barcode combinations. For profiling more cells in one sublibrary, we can increase the number of barcode combinations by increase the number of indexing of each round to 96, which can produce about 1 million combinations. To assess the fidelity of SPATAC-seq, we performed a species-mixing experiment with cultured human (K562) and mouse (Hepa) cells. Here, we tagged mixed permeabilized nuclei with only 8 barcoded transposome. In round 4, we generated eight sublibraries with different cell-recovery targets, which can be used to evaluating the stability of this method and the correlation between real doublet rates and theoretical value.