Multiplexed quantification of proteins and transcripts in single cells

By Vanessa M. Peterson, K. Zhang, Namit Kumar, Jerelyn Wong, Lixia Li, Douglas C. Wilson, Renée Moore, T. Mcclanahan, S. Sadekova, J. Klappenbach. Nature Biotechnology, 2017

763 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/efebd3eb2717297b198ebef1f23bae7921b3e1ae

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Citations in the corpus

High‐Dimensional Single‐Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease (2018, neutral): “However, regulation of mRNA and the encoded protein can differ dramatically and sc-RNaseqwill, depending on the analysis depth, detect only abundant transcripts (Peterson et al., 2017).”
Integrative single-cell analysis (2019, methodology): “Cell surface protein abundance can be captured using standard scRNA-seq methods by conjugating polyadenylated antibody barcodes to antibodies targeting cell surface proteins 20,21 (left panel).”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “Background is a type of technical bias that is characteristic of antibody-based measurements [6, 7, 27].”
Best practices for single-cell analysis across modalities (2023, background): “The authors of DSB found that this approach removes more noise owing to the availability of the background distribution in the raw counts 161 .”
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry (2018, methodology): “Future opportunities for integrating these data sets include droplet-based sequencing with oligonucleotide-tagged antibodies, including CITE-Seq (26), REAP-Seq (27), and AbSeq (28), which can be compared to this reference set of cell surface protein and transcriptome expression.”
Comprehensive integration of single-cell data (2018, neutral): “Our approach consists of four broad steps, as explained in detail below: (1) data preprocessing and feature selection, (2) dimension reduction and identification of "anchor" correspondences between datasets, (3) filtering, scoring, and weighting of anchor correspondences, (4) data matrix correction,”
Integrated analysis of multimodal single-cell data (2020, positive): “For example, although CITE-seq datasets can be analyzed by first identifying clusters based on gene expression values (Peterson et al., 2017; Stoeckius et al., 2017) and subsequently exploring their immunophenotypes, a multimodal computational workflow would define cell states based on both modaliti”