Tsang Lab @ Yale

We are active proponents and practictioners of data sharing and reuse. For every publication we lead, we provide the underlying data for reuse and verification upon publication - unless we do not have human subject consent, or the consent only allows controlled access in which case we use dbGaP (these are rare in our experience). Some of our datasets and tools are listed below. For other datasets, please see the data and code availability information in the manuscript.

Datasets

Baseline and early immune response following vaccination

>Single cell 3' CITE-seq (early custom protein panel covering 82 surface proteins and transcripome) https://github.com/niaid/baseline

Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus

Kotliarov Y*, Sparks R*, Martins A, Mulè M, Lu Y, Goswami M, Kardava L, Banchereau R, Pascual V, Biancotto A, Chen J, Schwartzberg P, Bansal N, Liu C, Cheung F, Moir S, Tsang JS. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nature Medicine 2020, 26: 618-629. PMID: 32094927, PMCID: PMC8392163, DOI: 10.1038/s41591-020-0769-8. (* co-first authors)

>Single cell 3' CITE-seq (early custom protein panel covering 82 surface proteins and transcripome) https://zenodo.org/records/10546916

Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint.

Mulè MP, Martins AJ, Cheung F, Farmer R, Sellers BA, Quiel JA, Jain A, Kotliarov Y, Bansal N, Chen J, Schwartzberg PL, Tsang JS. Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint. Immunity 2024 PMID: 38697118, DOI: 10.1016/j.immuni.2024.04.009.

>Single cell 5' CITE-seq (lyophilized Biolegend cocktail panel covering 130+ surface proteins, transcriptome, and TCR/BCR) and other omics and serology data) https://github.com/niaid/covid-flu

Influenza vaccination reveals sex dimorphic imprints of prior mild COVID-19

Sparks R*, Lau W*, Liu C*, Han K, Vrindten K, Sun G, Cox M, Andrews S, Bansal N, Failla L, Manischewitz J, Grubbs G, King L, Koroleva G, Leimenstoll S, Snow L, Chen J, Tang J, Mukherjee A, Sellers B, Apps R, McDermott A, Martins A, Bloch E, Golding H, Khurana S, Tsang JS. Influenza vaccination reveals sex dimorphic imprints of prior mild COVID-19. Nature 2023, 614: 752-761. PMID: 36599369, PMCID: PMC10481789, DOI: 10.1038/s41586-022-05670-5. (* co-first authors)

>Multimodal immune profiling data (flow cytometry, transcriptomics, antibody responses, etc.) from the following paper are available at here.

Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses

Tsang JS*, Schwartzberg P*, Kotliarov Y, Biancotto A, Xie Z, Germain R, Wang E, Olnes M, Narayanan M, Golding H, Moir S, Dickler H, Perl S, Cheung F, Center T, Consortium T, Obermoser G, Chaussabel D, Palucka K, Chen J, Fuchs J, Ho J, Khurana S, King L, Langweiler M, Liu H, Manischewitz J, Pos Z, Posada J, Schum P, Shi R, Valdez J, Wang W, Zhou H, Kastner D, Marincola F, McCoy J, Trinchieri G, Young N. Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses. Cell 2014, 157: 499-513. PMID: 24725414, PMCID: PMC4139290, DOI: 10.1016/j.cell.2014.03.031. (* senior and corresponding authors)

2. Time-resolved single cell 5' CITE-seq of COVID-19

>Single cell 5' CITE-seq (lyophilized Biolegend cocktail panel covering 130+ surface proteins, transcriptome, and TCR/BCR) and other omics and serology data: https://github.com/niaid/covid19-time-resolved

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

Liu C*, Martins AJ*, Lau WW*, Rachmaninoff N*, Chen J, Imberti L, Mostaghimi D, Fink DL, Burbelo PD, Dobbs K, Delmonte OM, Bansal N, Failla L, Sottini A, Quiros-Roldan E, Han KL, Sellers BA, Cheung F, Sparks R, Chun TW, Moir S, Lionakis MS; NIAID COVID Consortium; COVID Clinicians; Rossi C, Su HC, Kuhns DB, Cohen JI, Notarangelo LD, Tsang JS. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell. 2021 Apr 1;184(7):1836-1857.e22. doi: 10.1016/j.cell.2021.02.018. Epub 2021 Feb 10. PMID: 33713619; PMCID: PMC7874909. (* co-first authors)

Tools

Interactive figures for exploring linking cell-to-cell variations (cell population level) to human population variations (including raw data and Docker image)

Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations

Lu Y, Biancotto A, Cheung F, Remmers E, Shah N, McCoy J, Tsang JS. Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. Immunity 2016, 45: 1162-1175. PMID: 27851916, PMCID: PMC6532399, DOI: 10.1016/j.immuni.2016.10.025.

2. DSB: CITE-seq protein expression data normalization and denoising

Normalizing and denoising protein expression data from droplet-based single cell profiling

Mulè M*, Martins A*, Tsang JS. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nature Communications 2022, 13: 2099. PMID: 35440536, PMCID: PMC9018908, DOI: 10.1038/s41467-022-29356-8. (* co-first authors)

3. scGLMmr: hierarchical mixed effect model integration of population and single cell variations

Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint.

4. OMiCC: A crowdsourcing and online interactive tool for reusing and meta-analyzing public gene expression data

A crowdsourcing approach for reusing and meta-analyzing gene expression data

Shah N, Guo Y, Wendelsdorf K, Lu Y, Sparks R, Tsang JS. A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nature Biotechnology 2016, 34: 803-806. PMID: 27323300, PMCID: PMC6871002, DOI: 10.1038/nbt.3603.

We also illustrated, through a crowdsourcing experiment involving NIH volunteer scientists, how OMiCC can enable a group of non-computational biologists to utilize publicly available gene expression data to construct a multi-study “virtual” dataset of autoimmune diseases in both humans and animal models followed by meta-analysis to uncover disease signatures (Sparks et al Immunity 2016 and Lau et al F1000 Research 2016).

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