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Distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia

Nature Immunology volume 25, pages 1607–1622 (2024)Cite this article

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Abstract

The evolution of T cell molecular signatures in the distal lung of patients with severe pneumonia is understudied. Here, we analyzed T cell subsets in longitudinal bronchoalveolar lavage fluid samples from 273 patients with severe pneumonia, including unvaccinated patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or with respiratory failure not linked to pneumonia. In patients with SARS-CoV-2 pneumonia, activation of interferon signaling pathways, low activation of the NF-κB pathway and preferential targeting of spike and nucleocapsid proteins early after intubation were associated with favorable outcomes, whereas loss of interferon signaling, activation of NF-κB-driven programs and specificity for the ORF1ab complex late in disease were associated with mortality. These results suggest that in patients with severe SARS-CoV-2 pneumonia, alveolar T cell interferon responses targeting structural SARS-CoV-2 proteins characterize individuals who recover, whereas responses against nonstructural proteins and activation of NF-κB are associated with poor outcomes.

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Fig. 1: BAL T cell enrichment is associated with clinical outcome in SARS-CoV-2-P.
Fig. 2: BAL T cell IFN and NF-κB responses are associated with discharged and deceased status, respectively.
Fig. 3: BAL T cell NF-κB responses are associated with superinfection and worsened severity of illness.
Fig. 4: IFN signaling dominates the early BAL T cell response in SARS-CoV-2-P.
Fig. 5: The BAL T cell response to SARS-CoV-2-P is pathogen specific.
Fig. 6: BAL CD8+ T cells targeting nonstructural proteins associate with deceased status.
Fig. 7: Immunodominance and immunoprevalence of ORF1ab epitope targeting by BAL CD8+ T cells associate with deceased status.
Fig. 8: Pgen values of BAL CD8+ TCRs are associated with outcomes.

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Data availability

Patient-specific clinical metadata are available in Supplementary Data 2. Deidentified clinical information from this cohort is available on AccessClinicalData@NIAID (https://accessclinicaldata.niaid.nih.gov/study-viewer/clinical_trials/SCRIPT). Fluorescence-activated cell sorting cell-type proportions are available in Supplementary Data 1. Bulk RNA-sequencing data, including raw counts tables for CD4+ T cells, CD8+ T cells and Treg cells, are available in Supplementary Data 3, 4 and 5, respectively. Bulk TCR-sequencing data, including MiXCR-processed raw sequencing files for CD4+ T cells and CD8+ T cells, are available in Supplementary Data 9 and 10, respectively. Raw data for both bulk RNA sequencing and TCR sequencing are available in the dbGaP repository for the SCRIPT project (phs002300.v1.p1).

This study used reference genome GRCh38 for RNA-sequencing experiment data processing (available from iGenomes reference, originally donwloaded from NCBI Homo sapiens Annotation Release 106 (https://s3.amazonaws.com/igenomes.illumina.com/Homo_sapiens/NCBI/GRCh38/Homo_sapiens_NCBI_GRCh38.tar.gz), single-cell object from Grant et al. for bulk RNA-sequencing deconvolution (GSE155249, GSE155249_main.h5ad file), MIRA (https://adaptivepublic.blob.core.windows.net/publishedproject-supplements/covid-2020/ImmuneCODE-MIRA-Release002.1.zip) and IEDB (http://tools.iedb.org/mhci/download/ and http://tools.iedb.org/mhcii/download/) databases for TCR binding prediction.

Code availability

All code used for processing, analysis and figure generation is available at https://github.com/NUPulmonary/2024_Markov_Ren_Senkow.

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Acknowledgements

We thank the Robert H. Lurie Comprehensive Cancer Center of Northwestern University in Chicago for the use of the Flow Cytometry Core Facility. The Lurie Cancer Center is supported, in part, by an NCI Cancer Center Support Grant P30 CA060553. This research was supported, in part, through the computational resources and staff contributions provided for the Quest High-Performance Computing Facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research and Northwestern University Information Technology. We acknowledge the support of The Simpson Querrey Lung Institute for Translational Sciences at Northwestern University, the Dixon Translational Research Grants Initiative at Northwestern Medicine and the Northwestern University Clinical and Translational Sciences Institute (UL1TR001422). This work was also supported by the Chicago Biomedical Consortium with support from the Searle Funds at The Chicago Community Trust. N.S.M. was supported by AHA 24PRE1196998. C.A.G. was supported by T32HL076139 and F32HL162377. R.A.G. was funded by NIH grants T32AG020506 and F31AG071225 and Schmidt Science Fellows. A.V.M. was supported by NIH grants U19AI135964, U19AI181102, P01AG049665, P01HL154998, R01HL153312, R01HL158139, R01ES034350 and R21AG075423. R.G.W. was supported by NIH grants U19AI135964, U01TR003528, P01HL154998, R01HL149883 and R01LM013337. G.R.S.B. was supported by a Chicago Biomedical Consortium grant, Northwestern University Dixon Translational Science Award and Simpson Querrey Lung Institute for Translational Science and NIH grants AG049665, HL154998, HL14575, HL158139, HL147290, AG075423 and AI135964 and The Veterans Administration award I01CX001777. B.D.S. was supported by NIH awards R01HL149883, R01HL153122, P01HL154998, P01AG049665, U19AI135964 and U19AI181102. L.M.-N. was supported by the Parker B. Francis Opportunity Award and NIH awards K08HL15935 and U19AI135964.

Author information

Author notes

  1. These authors contributed equally: Ziyou Ren, Karolina J. Senkow.

  2. These authors contributed equally: Benjamin D. Singer, Luisa Morales-Nebreda.

Authors and Affiliations

  1. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Nikolay S. Markov, Ziyou Ren, Karolina J. Senkow, Rogan A. Grant, Catherine A. Gao, Elizabeth S. Malsin, Lango Sichizya, Hermon Kihshen, Kathryn A. Helmin, Milica Jovisic, Jason M. Arnold, Xóchitl G. Pérez-Leonor, Hiam Abdala-Valencia, Suchitra Swaminathan, Julu Nwaezeapu, Mengjia Kang, Alexander V. Misharin, Richard G. Wunderink, G. R. Scott Budinger, Benjamin D. Singer, Luisa Morales-Nebreda, Hiam Abdala-Valencia, Michael J. Alexander, Joseph I. Bailey, Thomas Bolig, Nicole Borkowski, Navdeep S. Chandel, Rebecca K. Clepp, John Coleman, Michael J. Cuttica, Thaddeus R. Cybulski, Jane E. Dematte, Joseph S. Deters, Alvaro Donayre, Helen K. Donnelly, Justin A. Fiala, Khalilah L. Gates, Cara J. Gottardi, Elen Gusman, SeungHye Han, Mishaal Hukamdad, Sydney M. Hyder, Manu Jain, Anthony M. Joudi, Ravi Kalhan, David W. Kamp, David A. Kidd, Zasu M. Klug, Erin A. Korth, Jacqueline M. Kruser, Romy Lawrence, Emily M. Leibenguth, Anne R. Levenson, Lindsey D. Gradone, Gabrielle Y. Liu, Theresa A. Lombardo, Ziyan Lu, Amy Ludwig, Ali Mahmoud, Alexandra C. McQuattie-Pimentel, Daniel Meza, Ruben J. Mylvaganam, Radhika Patel, Chiagozie O. Pickens, Yuliya Politanska, Taylor A. Poor, Michelle H. Prickett, Melissa Querrey, Karen M. Ridge, Madeline L. Rosenbaum, Sharon R. Rosenberg, Timothy Rowe, Susan R. Russell, Marc A. Sala, Clara J. Schroedl, Katharine Secunda, Sean Smith, Peter H. S. Sporn, Thomas Stoeger, Jacob I. Sznajder, Lindsey N. Textor, Rade Tomic, Betty Tran, Kaitlyn Vitale, Ajay A. Wagh, James M. Walter, Alexis R. Wolfe, Lisa F. Wolfe, Zhan Yu, Jose Castellanos, Lars Johnson, Scott Laurenzo, Gabrielle Matias, Emily M. Olson, Jamie Rowell, Ashley Smith-Nunez, Alison Szabo & Brian White

  2. Simpson Querrey Lung Institute for Translational Science, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Nikolay S. Markov, Ziyou Ren, Karolina J. Senkow, Rogan A. Grant, Catherine A. Gao, Elizabeth S. Malsin, Lango Sichizya, Hermon Kihshen, Kathryn A. Helmin, Milica Jovisic, Jason M. Arnold, Xóchitl G. Pérez-Leonor, Hiam Abdala-Valencia, Suchitra Swaminathan, Julu Nwaezeapu, Mengjia Kang, Alexander V. Misharin, Richard G. Wunderink, G. R. Scott Budinger, Benjamin D. Singer, Luisa Morales-Nebreda, Hiam Abdala-Valencia, Michael J. Alexander, Joseph I. Bailey, Thomas Bolig, Nicole Borkowski, Navdeep S. Chandel, Rebecca K. Clepp, John Coleman, Michael J. Cuttica, Thaddeus R. Cybulski, Jane E. Dematte, Joseph S. Deters, Alvaro Donayre, Helen K. Donnelly, Justin A. Fiala, Khalilah L. Gates, Cara J. Gottardi, Elen Gusman, SeungHye Han, Mishaal Hukamdad, Sydney M. Hyder, Manu Jain, Anthony M. Joudi, Ravi Kalhan, David W. Kamp, David A. Kidd, Zasu M. Klug, Erin A. Korth, Jacqueline M. Kruser, Romy Lawrence, Emily M. Leibenguth, Anne R. Levenson, Lindsey D. Gradone, Gabrielle Y. Liu, Theresa A. Lombardo, Ziyan Lu, Amy Ludwig, Ali Mahmoud, Alexandra C. McQuattie-Pimentel, Daniel Meza, Ruben J. Mylvaganam, Radhika Patel, Chiagozie O. Pickens, Yuliya Politanska, Taylor A. Poor, Michelle H. Prickett, Melissa Querrey, Karen M. Ridge, Madeline L. Rosenbaum, Sharon R. Rosenberg, Timothy Rowe, Susan R. Russell, Marc A. Sala, Clara J. Schroedl, Katharine Secunda, Sean Smith, Peter H. S. Sporn, Thomas Stoeger, Jacob I. Sznajder, Lindsey N. Textor, Rade Tomic, Betty Tran, Kaitlyn Vitale, Ajay A. Wagh, James M. Walter, Alexis R. Wolfe, Lisa F. Wolfe, Zhan Yu, Jose Castellanos, Lars Johnson, Scott Laurenzo, Gabrielle Matias, Emily M. Olson, Jamie Rowell, Ashley Smith-Nunez, Alison Szabo & Brian White

  3. Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Ziyou Ren

  4. Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Luke Rasmussen, Luke V. Rasmussen, Manoj Kandpal, Justin Starren & Firas Wehbe

  5. Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Egon A. Ozer, Ramon Lorenzo-Redondo, Judd F. Hultquist, Lacy M. Simons, Estefany Rios-Guzman, Judd Hultquist, Lacy Simmons & Estefany R. Guzman

  6. Center for Pathogen Genomics and Microbial Evolution, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Egon A. Ozer, Ramon Lorenzo-Redondo, Judd F. Hultquist, Lacy M. Simons, Estefany Rios-Guzman, Judd Hultquist, Lacy Simmons & Estefany R. Guzman

  7. Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Benjamin D. Singer, Elizabeth T. Bartom, Samuel W. M. Gatesy & Ali Shilatifard

  8. Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Benjamin D. Singer, Elizabeth T. Bartom & Ali Shilatifard

  9. Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Arghavan Alisoltanidehkordi, Alan R. Hauser & Sophia Nozick

  10. Division of Thoracic Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Ankit Bharat & Sanket Thakkar

  11. Robert H. Lurie Comprehensive Cancer Research Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Estefani Diaz & Emmy Jonasson

  12. Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Gaurav T. Gadhvi & Deborah R. Winter

  13. NU Clinical and Translational Sciences Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Pearl D. Go

  14. Genomics and Microbiome Core Facility, Rush University Medical Center, Chicago, IL, USA

    Stefan J. Green

  15. Department of Civil & Environmental Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Erica Marie Hartmann, Jiaxian Shen & Jack Sumner

  16. Department of Molecular Biosciences, Weinberg College of Arts & Sciences, Evanston, IL, USA

    Curt M. Horvath

  17. Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Rachel B. Kadar

  18. Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Jon W. Lomasney, Chao Qi, Elisheva D. Shanes & Anjana V. Yeldandi

  19. Northwestern Medicine Enterprise Data Warehouse, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Prasanth Nannapaneni, Anna E. Pawlowski & Daniel Schneider

  20. Department of Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Luís A. Nunes Amaral

  21. Division of Infectious Diseases, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Patrick C. Seed

Authors
  1. Nikolay S. Markov
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  2. Ziyou Ren
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  3. Karolina J. Senkow
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  4. Rogan A. Grant
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  5. Catherine A. Gao
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  6. Elizabeth S. Malsin
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  7. Lango Sichizya
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  8. Hermon Kihshen
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  9. Kathryn A. Helmin
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  10. Milica Jovisic
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  11. Jason M. Arnold
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  12. Xóchitl G. Pérez-Leonor
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  13. Hiam Abdala-Valencia
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  14. Suchitra Swaminathan
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  16. Mengjia Kang
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  18. Egon A. Ozer
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  19. Ramon Lorenzo-Redondo
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  20. Judd F. Hultquist
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  23. Alexander V. Misharin
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  24. Richard G. Wunderink
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  25. G. R. Scott Budinger
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  26. Benjamin D. Singer
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  27. Luisa Morales-Nebreda
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Consortia

The NU SCRIPT Study Investigators

  • Nikolay S. Markov
  • , Ziyou Ren
  • , Karolina J. Senkow
  • , Rogan A. Grant
  • , Catherine A. Gao
  • , Elizabeth S. Malsin
  • , Lango Sichizya
  • , Hermon Kihshen
  • , Kathryn A. Helmin
  • , Milica Jovisic
  • , Jason M. Arnold
  • , Xóchitl G. Pérez-Leonor
  • , Hiam Abdala-Valencia
  • , Suchitra Swaminathan
  • , Julu Nwaezeapu
  • , Mengjia Kang
  • , Luke V. Rasmussen
  • , Egon A. Ozer
  • , Ramon Lorenzo-Redondo
  • , Judd Hultquist
  • , Lacy Simmons
  • , Estefany R. Guzman
  • , Alexander V. Misharin
  • , Richard G. Wunderink
  • , G. R. Scott Budinger
  • , Benjamin D. Singer
  • , Luisa Morales-Nebreda
  • , Michael J. Alexander
  • , Arghavan Alisoltanidehkordi
  • , Joseph I. Bailey
  • , Elizabeth T. Bartom
  • , Ankit Bharat
  • , Thomas Bolig
  • , Nicole Borkowski
  • , Navdeep S. Chandel
  • , Rebecca K. Clepp
  • , John Coleman
  • , Michael J. Cuttica
  • , Thaddeus R. Cybulski
  • , Jane E. Dematte
  • , Joseph S. Deters
  • , Estefani Diaz
  • , Alvaro Donayre
  • , Helen K. Donnelly
  • , Justin A. Fiala
  • , Gaurav T. Gadhvi
  • , Khalilah L. Gates
  • , Samuel W. M. Gatesy
  • , Pearl D. Go
  • , Cara J. Gottardi
  • , Stefan J. Green
  • , Elen Gusman
  • , SeungHye Han
  • , Erica Marie Hartmann
  • , Alan R. Hauser
  • , Curt M. Horvath
  • , Mishaal Hukamdad
  • , Sydney M. Hyder
  • , Manu Jain
  • , Emmy Jonasson
  • , Anthony M. Joudi
  • , Rachel B. Kadar
  • , Ravi Kalhan
  • , David W. Kamp
  • , Manoj Kandpal
  • , David A. Kidd
  • , Zasu M. Klug
  • , Erin A. Korth
  • , Jacqueline M. Kruser
  • , Romy Lawrence
  • , Emily M. Leibenguth
  • , Anne R. Levenson
  • , Lindsey D. Gradone
  • , Gabrielle Y. Liu
  • , Jon W. Lomasney
  • , Theresa A. Lombardo
  • , Ziyan Lu
  • , Amy Ludwig
  • , Ali Mahmoud
  • , Alexandra C. McQuattie-Pimentel
  • , Daniel Meza
  • , Ruben J. Mylvaganam
  • , Prasanth Nannapaneni
  • , Sophia Nozick
  • , Luís A. Nunes Amaral
  • , Radhika Patel
  • , Anna E. Pawlowski
  • , Chiagozie O. Pickens
  • , Yuliya Politanska
  • , Taylor A. Poor
  • , Michelle H. Prickett
  • , Chao Qi
  • , Melissa Querrey
  • , Karen M. Ridge
  • , Madeline L. Rosenbaum
  • , Sharon R. Rosenberg
  • , Timothy Rowe
  • , Susan R. Russell
  • , Marc A. Sala
  • , Daniel Schneider
  • , Clara J. Schroedl
  • , Katharine Secunda
  • , Patrick C. Seed
  • , Elisheva D. Shanes
  • , Jiaxian Shen
  • , Ali Shilatifard
  • , Sean Smith
  • , Peter H. S. Sporn
  • , Justin Starren
  • , Thomas Stoeger
  • , Jack Sumner
  • , Jacob I. Sznajder
  • , Lindsey N. Textor
  • , Sanket Thakkar
  • , Rade Tomic
  • , Betty Tran
  • , Kaitlyn Vitale
  • , Ajay A. Wagh
  • , James M. Walter
  • , Firas Wehbe
  • , Deborah R. Winter
  • , Alexis R. Wolfe
  • , Lisa F. Wolfe
  • , Anjana V. Yeldandi
  • , Zhan Yu
  • , Jose Castellanos
  • , Lars Johnson
  • , Scott Laurenzo
  • , Gabrielle Matias
  • , Emily M. Olson
  • , Jamie Rowell
  • , Ashley Smith-Nunez
  • , Alison Szabo
  •  & Brian White

Contributions

Conception: A.V.M., R.G.W., G.R.S.B., B.D.S. and L.M.-N. Design: N.S.M., A.V.M., R.G.W., G.R.S.B., B.D.S. and L.M.-N. Implementation: N.S.M., Z.R., K.J.S., R.A.G., C.A.G., E.S.M., L.S., H.K., K.A.H., M.J., J.M.A., X.G.P.-L., H.A.-V., S.S., J.N., M.K., E.A.O., R.L.-R., J.F.H., L.M.S., E.R.-G., A.V.M., R.G.W., B.D.S. and L.M.-N. Data analysis/interpretation: N.S.M., Z.R., K.J.S., E.S.M., A.V.M., R.G.W., G.R.S.B., B.D.S. and L.M.-N. Drafting the article: Z.R., K.J.S., B.D.S. and L.M.-N. Revising/reviewing the article: N.S.M., G.R.S.B., B.D.S. and L.M.-N. Public responsibility: L.R., R.G.W., B.D.S. and L.M.-N.

Corresponding author

Correspondence to Luisa Morales-Nebreda.

Ethics declarations

Competing interests

B.D.S. holds US patent number US 10,905,706 B2, ‘Compositions and methods to accelerate resolution of acute lung inflammation’, and serves on the Scientific Advisory Board of Zoe Biosciences, outside of the submitted work. The other authors have no competing interests to declare.

Peer review

Peer review information

Nature Immunology thanks Sophie Valkenburg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ioana Staicu, in collaboration with the Nature Immunology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Graphical abstract and the study cohort.

(a) Graphical abstract. (b) CONSORT diagram of patients included in this study. (c) Schematic depicting multi-step analysis of BAL fluid samples with flow cytometry, bulk RNA-sequencing, and bulk TCR-sequencing by diagnosis (NPC, OP, SARS-CoV-2-P, OVP) and T cell subset (CD4+, CD8+, Treg).

Extended Data Fig. 2 SARS-CoV-2-P is characterized by a lymphomonocytic alveolar infiltrate early following intubation.

(a) Flow cytometry analysis of BAL immune cell subset composition in CD3ε+CD4+ T cells, CD3ε+CD8+ T cells, Treg cells (CD3ε+CD4+CD25+CD127−), monocytes (HLA-DR+CD4+CD206−), macrophages (CD206+), and neutrophils (CD15+) indicating whether a given patient was discharged or deceased, the duration of mechanical ventilation (range 0–110 days; blanks indicate chronically ventilated patients), and presence (superinfection) or absence of bacterial infection with columns ordered by diagnosis (n = 432 samples (NPC [n = 36], OP [n = 187], SARS-CoV-2-P [n = 165], and OVP [n = 44]) and then by days from intubation when the sample was obtained. VAP (ventilator-associated pneumonia) designates samples from NPC or patients with SARS-CoV-2-P or OVP who cleared the virus and then developed bacterial pneumonia. Each column represents a BAL sample. (b) Box plot of percentage of BAL CD4+ T cells detected in BAL fluid samples from diagnosis groups (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (c) As in b, for CD8+ T cells. (d) As in b, for Treg cells. (e) As in b, for monocytes. (f) As in b, for macrophages. (g) As in b, for neutrophils. (h) Box plot of BAL CD4+ T cell percentages between early (≤48 hours following intubation) and late (>48 hours following intubation) samples by diagnosis (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (i) As in h, for CD8+ T cells. (j) As in h, for Treg cells. (k) Box plot of BAL CD3+ T cell percentage grouped by the presence or absence of bacterial superinfection in early (≤48 hours following intubation) and late (>48 hours following intubation) SARS-CoV-2-P samples (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (l) As in i, for monocytes. (m) Spearman correlation analysis between the percentage of BAL monocytes and the duration of mechanical ventilation by diagnosis. (n) As in i, for neutrophils. (o) As in m, for neutrophils. (p) As in k, for neutrophils. (q) As in i, for macrophages. (r) As in m, for macrophages.

Extended Data Fig. 3 Persistent BAL T cell enrichment is associated with discharged status.

(a) Box plot of BAL CD4+ T cell percentage by discharged versus deceased status. n = 432 samples (NPC [n = 36], OP [n = 187], SARS-CoV-2-P [n = 165], and OVP [n = 44]). q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction. (b) As in a, for CD8+ T cells. (c) As in a, for Treg cells. (d) As in a, for macrophages. (e) As in a, for monocytes. (f) As in a, grouped by discharged and deceased status and by early (≤48 hours following intubation) and late (>48 hours following intubation) timing of BAL sampling relative to intubation. (g) As in f, for CD8+ T cells. (h) As in f, for Treg cells. (i) As in f, for monocytes. (j) As in f, for macrophages. (k) As in a, for neutrophils. (l) As in f, for neutrophils.

Extended Data Fig. 4 BAL immune cell subset abundance and phenotype correlate with clinical features.

(a) Correlation analysis between the percentage of BAL immune cell subsets and clinical, physiological, and laboratory variables in NPC. No significant values after calculating Spearman rank correlation coefficient with FDR correction. (b) As in a, for OP. (c) As in a, for OVP. (d) Proportion of SARS-CoV-2-P BAL fluid samples, comparing presence or absence of bacterial superinfection with discharged versus deceased status. Nonsignificant by Fisher exact test. (e) Proportion of SARS-CoV-2-P BAL fluid samples, comparing pneumonia episode outcome status (cured, indeterminate, not cured) with discharged versus deceased status (q < 0.05, Fisher exact test with FDR correction). (f) Correlation analysis between BAL CD4+ and CD8+ T cell surface expression of CD127 and HLA-DR and clinical, laboratory, and physiological variables in SARS-CoV-2-P samples. Spearman rank correlation coefficient with FDR correction (q < 0.05 [*]). (g) Left: Correlation plot of CD4+ T cell surface expression of CD127 against the duration of mechanical ventilation. Spearman rank correlation coefficient shown. Shaded area represents 95% CI. Second from left: As in left, for CD4+ T cell surface expression of HLA-DR. Second from right: As in left, for CD8+ T cell surface expression of CD127. Right: As in left, for CD8+ T cell surface expression of HLA-DR.

Extended Data Fig. 5 BAL T cells in SARS-CoV-2-P are transcriptionally enriched for immune processes compared with NPC, OP, and OVP.

(a) Number of patients contributing BAL samples for RNA-seq analysis (n = 336 samples; NPC [n = 38], OP [n = 51], SARS-CoV-2-P [n = 200], and OVP [n = 47]). (b) Proportion of samples grouped by T cell subset and diagnosis. (c) Number of samples grouped by early (≤48 hours following intubation) and late (>48 hours following intubation) timing of BAL sampling relative to intubation or obtained in patients receiving chronic ventilation. (d) Box plot of mean Sequential Organ Failure Assessment (SOFA) scores over the day of the BAL. Nonsignificant after pairwise Wilcoxon rank-sum tests with FDR correction). (e) Proportion of discharged versus deceased patients. Nonsignificant after pairwise χ2 tests for homogeneity of proportions with FDR correction. (f) Proportion of sex (pairwise χ2 tests for homogeneity of proportions with FDR correction). (g) Top: K-means clustering of 975 differentially expressed genes (q < 0.05, likelihood-ratio test with FDR correction) in CD8+ T cell samples. Columns represent unique samples and column headers are color-coded by diagnosis, discharged versus deceased status, duration of mechanical ventilation (range 0–90 days, blanks indicate chronically ventilated patients), and superinfection status with columns ordered by NPC (n = 15), OP (n = 15), SARS-CoV-2-P (n = 72), and OVP (n = 23) and then by days from intubation when the sample was obtained. VAP (ventilator-associated pneumonia) designates samples from NPC or patients with SARS-CoV-2-P or OVP who cleared the virus and then developed bacterial pneumonia. Representative genes are shown for each cluster. Bottom: As in top, for CD4+ T cell samples (866 differentially expressed genes) in NPC (n = 14), OP (n = 23), SARS-CoV-2-P (n = 81), and OVP (n = 20). (h) Top: GSEA of Hallmark gene sets for the pairwise comparison between SARS-CoV-2-P samples and OVP samples in CD8+ T cells from g, top. Count denotes pathway size after removing genes not detected in the expression dataset. Enrichment denotes significant (q < 0.25 with FDR correction) upregulated (red) and downregulated (blue) pathways by normalized enrichment score. Bottom: As in h, top for CD4+ T cells from g, bottom. (i) Left: Gene ontology (GO) parent term annotation (q < 0.05 with FDR correction) from cluster 2_CD8. Points are color-coded by unique terms and size denotes the number of genes within each GO term. Right: As in left, for cluster 1_CD8. (j) As in I for cluster 1_CD4.

Extended Data Fig. 6 BAL T cells have an activated memory phenotype during severe pneumonia.

(a) K-means clustering of 80 differentially expressed genes (q < 0.05, likelihood-ratio test with FDR correction) between NPC (n = 9), OP (n = 13), SARS-CoV-2-P (n = 47), and OVP (n = 4) in Treg cell samples. Columns represent unique samples and column headers are color-coded by diagnosis, discharged versus deceased status, duration of mechanical ventilation (range 0–90, blanks indicate chronically ventilated patients), and superinfection status. VAP (ventilator-associated pneumonia) designates samples from NPC or patients with SARS-CoV-2-P or OVP who cleared the virus and then developed bacterial pneumonia. Samples were clustered using Euclidean distance and Ward’s minimum variance linkage method. Representative genes are shown for each cluster. (b) GSEA of Hallmark gene sets for the pairwise comparison between SARS-CoV-2-P samples and NPC, OP, and OVP samples in Treg samples from a. Count denotes pathway size after removing genes not detected in the expression dataset. Enrichment denotes significant (q < 0.25 with FDR correction) upregulated (red) and downregulated (blue) pathways by normalized enrichment score. (c) As in b, for the pairwise comparison between SARS-CoV-2-P samples and OVP samples. (d) Deconvolution analysis showing inferred proportion of BAL CD8+ T cell subsets. Column data labels are as in a. (e) Proportion of inferred BAL CD8+ T cell subsets by diagnosis. (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (f) Proportion of inferred BAL CD8+ T cell subsets by discharged versus deceased status and early (≤48 hours following intubation) and late (>48 hours following intubation) timing of BAL sampling relative to intubation in SARS-CoV-2-P samples (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (g) As in d, for CD4+ T cell subsets. (h) As in e, for CD4+ T cell subsets. (i) As in f, for CD4+ T cell subsets.

Extended Data Fig. 7 TCR repertoire diversity is lower in SARS-CoV-2-P samples complicated by superinfection or VAP.

(a) Number of patients contributing BAL samples for TCR-seq analysis (n = 130 samples; NPC [n = 13], OP [n = 16], SARS-CoV-2-P [n = 73], and OVP [n = 28]). (b) Proportion of TCR-seq samples grouped by T cell subset and diagnosis. (c) Number of TCR-seq samples grouped by early (≤48 hours following intubation) and late (>48 hours following intubation) timing of BAL sampling relative to intubation. (d) Box plot of the mean Sequential Organ Failure Assessment (SOFA) scores on the day of BAL sample. Nonsignificant after pairwise Wilcoxon rank-sum tests with FDR correction. (e) Proportion of discharged versus deceased status patients. Nonsignificant after pairwise χ2 tests for homogeneity of proportions with FDR correction. (f) Proportion of sex (pairwise χ2 tests for homogeneity of proportions with FDR correction. (g) TCR richness analysis showing Chao 1 values in combined BAL CD4+ and CD8+ T cells by diagnosis. (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). (h) As in g, grouped by early (≤48 hours following intubation) and late (>48 hours following intubation) timing of BAL sampling relative to intubation. (i) As in g, grouped by discharged versus deceased status. (j) Pearson correlation analysis between TCR richness (Chao 1 values) and patient age in combined BAL CD4+ and CD8+ T cells in SARS-CoV-2-P samples (left) and NPC, OP, and OVP samples (right). Shaded area represents 95% CI. (k) As in j, correlated with the duration of mechanical ventilation. (l) Richness analysis showing Chao 1 values in SARS-CoV-2-P samples grouped by discharged versus deceased status in primary SARS-CoV-2 infection only (top), bacterial superinfection (middle), and VAP (bottom). Wilcoxon rank sum tests.

Extended Data Fig. 8 BAL T cells in SARS-CoV-2-P are virus specific in HLA context.

(a) Proportion of HLA-A molecules identified in CD8+ T cells in SARS-CoV-2-P samples (n of patients = 14). (b) As in a, for HLA-B. (c) Left: Proportion of TCRβ (V) gene usage in CD8+ TCR sequences in SARS-CoV-2-P samples (n of patients = 14). Middle: Absolute number of TCRβ sequences for each V region. Right: Representative epitopes from TCR chains exhibiting a count > 5 per dominant gene (TRBV12-3 and TRBV27). (d) As in c, for CD4+ TCR sequences. Right: Dominant genes (TCR count > 30) and representative epitopes are annotated for TRBV6-6 and TRBV20-1. (e) Proportion of HLA-A allele representation for detected CD8+ TCRβ (V) segments. (f) As in e, for HLA-B. (g) Heatmap of association measure of HLA alleles distribution within distinct V genes (1 – normalized Shannon entropy). (h) Proportion of TCRs detected by patient with distinct V gene segments.

Extended Data Fig. 9 Generation probabilities (Pgen) of BAL CD4+ TCRs is lower in SARS-CoV-2-P than in OVP, OP, and NPC.

(a) Logarithmic distribution of Pgen for post-GLIPH2-enriched CDR3β amino acid sequences across diagnoses (q < 0.05, pairwise Wilcoxon rank-sum tests with FDR correction). Each dot corresponds to a TCRβ chain. (b) Probability density of the data in a. (c) Logarithmic distribution of Pgen for post-GLIPH2-enriched and cross-referenced CDR3β amino acid sequences to MIRA MHCII dataset in SARS-CoV-2-P and OP. Each dot corresponds to a TCRβ chain. Nonsignificant after Wilcoxon rank-sum test. (d) Probability density of the data in c. (e) Similarity network analysis of BAL CD4+ TCR sequences cross-referenced to MIRA MHCII dataset epitope pools. Nodes represent unique TCR (CDR3β) sequences and are color-coded by diagnosis status (non-SARS-CoV-2-P [NPC, OP, and OVP], SARS-CoV-2, or shared). Edges connect TCR sequences belonging to the same patterns or specificity groups identified through the GLIPH2 algorithm. Dot size represents calculated generation probability (Pgen) of individual TCRβ sequences. Left: Representative TCR sequences from prominent clusters are annotated.

Extended Data Table 1 Study cohort and list of detected pathogens
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Fluorescence-activated cell sorting cell-type proportions in BAL samples.

Supplementary Data 2

Anonymized metadata by patient and samples for bulk RNA sequencing.

Supplementary Data 3

Raw counts for CD4+ T cell bulk RNA-sequencing samples.

Supplementary Data 4

Raw counts for CD8+ T cell bulk RNA-sequencing samples.

Supplementary Data 5

Raw counts for Treg cell bulk RNA-sequencing samples.

Supplementary Data 6

a, Differentially expressed genes in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD8). b, Differentially expressed genes in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 2_CD8). c, Differentially expressed genes in CD4+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD4). d, Differentially expressed genes in CD4+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 2_CD4).

Supplementary Data 7

a, Differential expression analysis in CD8+ T cells of pairwise comparisons between SARS-CoV-2-P samples and combined NPC, OP and OVP samples. b, Differential expression analysis in CD4+ T cells of pairwise comparisons between SARS-CoV-2-P samples and combined NPC, OP and OVP samples. c, Differential expression analysis in CD8+ T cells of pairwise comparisons between SARS-CoV-2-P samples and OVP samples. d, Differential expression analysis in CD4+ T cells of pairwise comparisons between SARS-CoV-2-P samples and OVP samples. e, Differential expression analysis in Treg cells of pairwise comparisons between SARS-CoV-2-P samples and combined NPC, OP and OVP samples. f, Differential expression analysis in Treg cells of pairwise comparisons between SARS-CoV-2-P samples and OVP samples. g, Differential expression analysis in CD4+ T cells of pairwise comparisons between early (≤48 h after intubation) and late (>48 h after intubation) SARS-CoV-2-P samples. h, Differential expression analysis in CD8+ T cells of pairwise comparisons between early (≤48 h after intubation) and late (>48 h after intubation) SARS-CoV-2-P samples.

Supplementary Data 8

a, Significant GO processes identified in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD8). b, Significant GO processes identified in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 2_CD8). c, Significant GO parent terms and terms identified in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD8). d, Significant GO parent terms and terms identified in CD8+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 2_CD8). e, Significant GO processes identified in CD4+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD4). f, Significant GO parent terms and terms identified in CD4+ T cell bulk RNA-sequencing samples from k-means clustering analysis (cluster 1_CD4).

Supplementary Data 9

MiXCR-processed raw sequencing files with TCR repertoire data for CD4+ T cell bulk TCR-sequencing samples.

Supplementary Data 10

MiXCR-processed raw sequencing files with TCR repertoire data for CD8+ T cell bulk TCR-sequencing samples.

Supplementary Data 11

a, Anonymized metadata by patient and samples for bulk TCR sequencing. b, GLIPH2 analysis establishing specificity groups from CD4+ T cell bulk TCR-sequencing samples. c, GLIPH2 analysis establishing specificity groups from CD8+ T cell bulk TCR-sequencing samples.

Supplementary Data 12

a, Cross-reference of CD4+ T cell repertoire sequences with the MIRA dataset. b, Cross-reference of CD8+ T cell repertoire sequences with the MIRA dataset. c, MIRA MHC class I and MHC class II peptide pools identified in a patient cohort. d, Hierarchical distribution of MHC class I immunodominant epitopes by patient from the bulk TCR-sequencing subset. e, Pairwise similarity and average conservation score estimation between SARS-CoV-2 and HCoV epitopes.

Supplementary Data 13

a, Distribution of Pgen values for CD8+ T cells metadata. b, Distribution of Pgen values for CD4+ T cells metadata. c, Similarity network analysis of CD8+ T cells metadata. d, Similarity network analysis of CD4+ T cells metadata.

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Markov, N.S., Ren, Z., Senkow, K.J. et al. Distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia. Nat Immunol 25, 1607–1622 (2024). https://doi.org/10.1038/s41590-024-01914-w

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