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Robust single-cell matching and multimodal analysis using shared and distinct features.
Zhu, Bokai; Chen, Shuxiao; Bai, Yunhao; Chen, Han; Liao, Guanrui; Mukherjee, Nilanjan; Vazquez, Gustavo; McIlwain, David R; Tzankov, Alexandar; Lee, Ivan T; Matter, Matthias S; Goltsev, Yury; Ma, Zongming; Nolan, Garry P; Jiang, Sizun.
  • Zhu B; Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
  • Chen S; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Bai Y; Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, USA.
  • Chen H; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Liao G; Department of Chemistry, Stanford University, Stanford, CA, USA.
  • Mukherjee N; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Vazquez G; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • McIlwain DR; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Tzankov A; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Lee IT; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Matter MS; Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Goltsev Y; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Ma Z; Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Nolan GP; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Jiang S; Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, USA. zongming@wharton.upenn.edu.
Nat Methods ; 20(2): 304-315, 2023 02.
Article in English | MEDLINE | ID: covidwho-2185967
ABSTRACT
The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Proteomics / COVID-19 Type of study: Randomized controlled trials Limits: Humans Language: English Journal: Nat Methods Journal subject: Laboratory Techniques and procedures Year: 2023 Document Type: Article Affiliation country: S41592-022-01709-7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Proteomics / COVID-19 Type of study: Randomized controlled trials Limits: Humans Language: English Journal: Nat Methods Journal subject: Laboratory Techniques and procedures Year: 2023 Document Type: Article Affiliation country: S41592-022-01709-7