Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia.
Humans
Leukemia, Myeloid, Acute
/ drug therapy
Drug Resistance, Neoplasm
/ genetics
Single-Cell Analysis
Sulfonamides
/ pharmacology
Bridged Bicyclo Compounds, Heterocyclic
/ pharmacology
CD36 Antigens
/ metabolism
Female
Male
Antineoplastic Agents
/ pharmacology
Middle Aged
Proto-Oncogene Proteins c-bcl-2
/ metabolism
Aged
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
08
03
2024
accepted:
10
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research.
Identifiants
pubmed: 39477946
doi: 10.1038/s41467-024-53535-4
pii: 10.1038/s41467-024-53535-4
doi:
Substances chimiques
venetoclax
N54AIC43PW
Sulfonamides
0
Bridged Bicyclo Compounds, Heterocyclic
0
CD36 Antigens
0
Antineoplastic Agents
0
Proto-Oncogene Proteins c-bcl-2
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9402Investigateurs
Rudolf Aebersold
(R)
Melike Ak
(M)
Faisal S Al-Quaddoomi
(FS)
Silvana I Albert
(SI)
Jonas Albinus
(J)
Ilaria Alborelli
(I)
Sonali Andani
(S)
Per-Olof Attinger
(PO)
Marina Bacac
(M)
Daniel Baumhoer
(D)
Beatrice Beck-Schimmer
(B)
Niko Beerenwinkel
(N)
Christian Beisel
(C)
Lara Bernasconi
(L)
Anne Bertolini
(A)
Bernd Bodenmiller
(B)
Ximena Bonilla
(X)
Lars Bosshard
(L)
Byron Calgua
(B)
Natalia Chicherova
(N)
Maya D'Costa
(M)
Esther Danenberg
(E)
Natalie R Davidson
(NR)
Monica-Andreea Drăgan
(MA)
Reinhard Dummer
(R)
Stefanie Engler
(S)
Martin Erkens
(M)
Katja Eschbach
(K)
Cinzia Esposito
(C)
André Fedier
(A)
Pedro F Ferreira
(PF)
Joanna Ficek-Pascual
(J)
Anja L Frei
(AL)
Bruno Frey
(B)
Sandra Goetze
(S)
Linda Grob
(L)
Gabriele Gut
(G)
Detlef Günther
(D)
Pirmin Haeuptle
(P)
Viola Heinzelmann-Schwarz
(V)
Sylvia Herter
(S)
Rene Holtackers
(R)
Tamara Huesser
(T)
Alexander Immer
(A)
Anja Irmisch
(A)
Tim M Jaeger
(TM)
Katharina Jahn
(K)
Alva R James
(AR)
Philip M Jermann
(PM)
André Kahles
(A)
Abdullah Kahraman
(A)
Viktor H Koelzer
(VH)
Werner Kuebler
(W)
Jack Kuipers
(J)
Christian P Kunze
(CP)
Christian Kurzeder
(C)
Kjong-Van Lehmann
(KV)
Mitchell Levesque
(M)
Flavio C Lombardo
(FC)
Sebastian Lugert
(S)
Gerd Maass
(G)
Philipp Markolin
(P)
Martin Mehnert
(M)
Julien Mena
(J)
Julian M Metzler
(JM)
Nicola Miglino
(N)
Holger Moch
(H)
Simone Muenst
(S)
Riccardo Murri
(R)
Charlotte K Y Ng
(CKY)
Stefan Nicolet
(S)
Marta Nowak
(M)
Monica Nunez Lopez
(MN)
Patrick G A Pedrioli
(PGA)
Lucas Pelkmans
(L)
Salvatore Piscuoglio
(S)
Michael Prummer
(M)
Laurie Prélot
(L)
Natalie Rimmer
(N)
Mathilde Ritter
(M)
Christian Rommel
(C)
María L Rosano-González
(ML)
Gunnar Rätsch
(G)
Natascha Santacroce
(N)
Jacobo Sarabia Del Castillo
(JS)
Ramona Schlenker
(R)
Petra C Schwalie
(PC)
Severin Schwan
(S)
Tobias Schär
(T)
Gabriela Senti
(G)
Wenguang Shao
(W)
Franziska Singer
(F)
Berend Snijder
(B)
Bettina Sobottka
(B)
Vipin T Sreedharan
(VT)
Stefan Stark
(S)
Daniel J Stekhoven
(DJ)
Tanmay Tanna
(T)
Tinu M Thomas
(TM)
Markus Tolnay
(M)
Vinko Tosevski
(V)
Nora C Toussaint
(NC)
Mustafa A Tuncel
(MA)
Marina Tusup
(M)
Audrey Van Drogen
(A)
Marcus Vetter
(M)
Tatjana Vlajnic
(T)
Sandra Weber
(S)
Walter P Weber
(WP)
Rebekka Wegmann
(R)
Michael Weller
(M)
Fabian Wendt
(F)
Norbert Wey
(N)
Mattheus H E Wildschut
(MHE)
Shuqing Yu
(S)
Johanna Ziegler
(J)
Marc Zimmermann
(M)
Martin Zoche
(M)
Gregor Zuend
(G)
Informations de copyright
© 2024. The Author(s).
Références
Roboz, G. J. et al. International randomized phase III study of elacytarabine versus investigator choice in patients with relapsed/refractory acute myeloid leukemia. J. Clin. Oncol. 32, 1919–1926 (2014).
pubmed: 24841975
doi: 10.1200/JCO.2013.52.8562
Bewersdorf, J. P. et al. Venetoclax-based salvage therapy in patients with relapsed/refractory acute myeloid leukemia previously treated with FLT3 or IDH1/2 inhibitors. Leuk. Lymphoma 64, 188–196 (2023).
pubmed: 36287540
doi: 10.1080/10428194.2022.2136952
Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020).
pubmed: 33087716
pmcid: 7577981
doi: 10.1038/s41467-020-19119-8
van Galen, P. et al. Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Cell 176, 1265–1281.e24 (2019).
pubmed: 30827681
pmcid: 6515904
doi: 10.1016/j.cell.2019.01.031
Miles, L. A. et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature 587, 477–482 (2020).
pubmed: 33116311
pmcid: 7677169
doi: 10.1038/s41586-020-2864-x
Wu, J. et al. A single-cell survey of cellular hierarchy in acute myeloid leukemia. J. Hematol. Oncol. 13, 128 (2020).
pubmed: 32977829
pmcid: 7517826
doi: 10.1186/s13045-020-00941-y
Beneyto-Calabuig, S. et al. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. Cell Stem Cell 30, 706–721.e8 (2023).
pubmed: 37098346
doi: 10.1016/j.stem.2023.04.001
Tyner, J. W. et al. Kinase pathway dependence in primary human leukemias determined by rapid inhibitor screening. Cancer Res. 73, 285–296 (2013).
pubmed: 23087056
doi: 10.1158/0008-5472.CAN-12-1906
Kurtz, S. E. et al. Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies. Proc. Natl Acad. Sci. USA 114, E7554–E7563 (2017).
pubmed: 28784769
pmcid: 5594650
doi: 10.1073/pnas.1703094114
Pemovska, T. et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).
pubmed: 24056683
doi: 10.1158/2159-8290.CD-13-0350
Bottomly, D. et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell 40, 850–864.e9 (2022).
pubmed: 35868306
pmcid: 9378589
doi: 10.1016/j.ccell.2022.07.002
Bhatt, S. et al. Reduced mitochondrial apoptotic priming drives resistance to BH3 mimetics in acute myeloid leukemia. Cancer Cell 38, 872–890.e6 (2020).
pubmed: 33217342
doi: 10.1016/j.ccell.2020.10.010
Snijder, B. et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 4, e595–e606 (2017).
pubmed: 29153976
pmcid: 5719985
doi: 10.1016/S2352-3026(17)30208-9
Kuusanmäki, H. et al. Ex vivo venetoclax sensitivity testing predicts treatment response in acute myeloid leukemia. Haematologica. https://doi.org/10.3324/HAEMATOL.2022.281692 (2022).
Kuusanmäki, H. et al. Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica 105, 708–720 (2020).
pubmed: 31296572
pmcid: 7049363
doi: 10.3324/haematol.2018.214882
Spinner, M. A. et al. Ex vivo drug screening defines novel drug sensitivity patterns for informing personalized therapy in myeloid neoplasms. https://doi.org/10.1182/bloodadvances.2020001934 (2020).
Lin, L. et al. Ex-vivo drug testing predicts chemosensitivity in acute myeloid leukemia. J. Leukoc. Biol. 107, 859–870 (2020).
pubmed: 32293060
doi: 10.1002/JLB.5A0220-676RR
Liebers, N. et al. Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial. Nat. Cancer. https://doi.org/10.1038/s43018-023-00645-5 (2023).
Malani, D. et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 12, 388–401 (2022).
pubmed: 34789538
doi: 10.1158/2159-8290.CD-21-0410
Kornauth, C. et al. Functional precision medicine provides clinical benefit in advanced aggressive hematologic cancers and identifies exceptional responders. Cancer Discov. 12, 372–387 (2022).
pubmed: 34635570
doi: 10.1158/2159-8290.CD-21-0538
Schmid, J. A. et al. Efficacy and feasibility of pharmacoscopy-guided treatment for acute myeloid leukemia patients who have exhausted all registered therapeutic options. Haematologica. https://doi.org/10.3324/haematol.2023.283224 (2023).
Heinemann, T. et al. Deep morphology learning enhances ex vivo drug profiling-based precision medicine. Blood Cancer Discov. 3, 502–515 (2022).
pubmed: 36125297
pmcid: 9894727
doi: 10.1158/2643-3230.BCD-21-0219
Zeng, A. G. X. et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat. Med. 28, 1212–1223 (2022).
pubmed: 35618837
doi: 10.1038/s41591-022-01819-x
Jayavelu, A. K. et al. The proteogenomic subtypes of acute myeloid leukemia. Cancer Cell. 1–17 https://doi.org/10.1016/j.ccell.2022.02.006 (2022).
Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021).
pubmed: 33482122
doi: 10.1016/j.ccell.2021.01.004
Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
Kropivsek, K. et al. Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma. Nat. Cancer 4, 734–753 (2023).
pubmed: 37081258
pmcid: 10212768
doi: 10.1038/s43018-023-00544-9
Wildschut, M. H. E. et al. Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis. Nat. Commun. 14, 6414 (2023).
pubmed: 37828014
pmcid: 10570306
doi: 10.1038/s41467-023-42101-z
Cancer Genome Atlas Research Network. et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
doi: 10.1056/NEJMoa1301689
Tyner, J. W. et al. Functional genomic landscape of acute myeloid leukaemia. Nature 19, 28 (2018).
Roberts, A. W. & Huang, D. Targeting BCL2 with BH3 mimetics: basic science and clinical application of venetoclax in chronic lymphocytic leukemia and related b cell malignancies. Clin. Pharmacol. Ther. 101, 89–98 (2017).
pubmed: 27806433
doi: 10.1002/cpt.553
Konopleva, M. et al. Efficacy and biological correlates of response in a phase II study of venetoclax monotherapy in patients with acute myelogenous leukemia. Cancer Discov. 6, 1106–1117 (2016).
pubmed: 27520294
pmcid: 5436271
doi: 10.1158/2159-8290.CD-16-0313
Sullivan, G. P., Flanagan, L., Rodrigues, D. A. & Ní Chonghaile, T. The path to venetoclax resistance is paved with mutations, metabolism, and more. Sci. Transl. Med. 14, eabo6891 (2022).
Ong, F., Kim, K. & Konopleva, M. Y. Venetoclax resistance: mechanistic insights and future strategies. Cancer Drug Resist. 5, 380–400 (2022).
pubmed: 35800373
pmcid: 9255248
doi: 10.20517/cdr.2021.125
Stelmach, P. & Trumpp, A. Leukemic stem cells and therapy resistance in acute myeloid leukemia. Haematologica 108, 353–366 (2023).
pubmed: 36722405
pmcid: 9890038
doi: 10.3324/haematol.2022.280800
Dhakal, P. et al. Acute myeloid leukemia resistant to venetoclax-based therapy: What does the future hold? Blood Rev. 59, 101036 (2023).
pubmed: 36549969
doi: 10.1016/j.blre.2022.101036
Salah, H. T., Dinardo, C. D., Konopleva, M. & Khoury, J. D. Potential biomarkers for treatment response to the bcl-2 inhibitor venetoclax: State of the art and future directions. Cancers 13, 1–12 (2021).
doi: 10.3390/cancers13122974
DiNardo, C. D. et al. Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML. Blood 135, 791–803 (2020).
pubmed: 31932844
pmcid: 7068032
doi: 10.1182/blood.2019003988
Zhang, H. et al. Integrated analysis of patient samples identifies biomarkers for venetoclax efficacy and combination strategies in acute myeloid leukemia. Nat. Cancer 1, 826 (2020).
pubmed: 33123685
pmcid: 7591155
doi: 10.1038/s43018-020-0103-x
Chen, X. et al. Targeting mitochondrial structure sensitizes acute myeloid leukemia to venetoclax treatment. Cancer Discov. 9, 890–909 (2019).
pubmed: 31048321
pmcid: 6606342
doi: 10.1158/2159-8290.CD-19-0117
Stevens, B. M. et al. Fatty acid metabolism underlies venetoclax resistance in acute myeloid leukemia stem cells. Nat. Cancer 1, 1176–1187 (2020).
pubmed: 33884374
pmcid: 8054994
doi: 10.1038/s43018-020-00126-z
Foroutan, M. et al. Single sample scoring of molecular phenotypes. BMC Bioinforma. 19, 404 (2018).
doi: 10.1186/s12859-018-2435-4
Silverstein, R. L. & Febbraio, M. CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior. Sci. Signal. 2, re3 (2009).
pubmed: 19471024
pmcid: 2811062
doi: 10.1126/scisignal.272re3
Jones, C. L. et al. Inhibition of amino acid metabolism selectively targets human leukemia stem cells. Cancer Cell 34, 724 (2018).
pubmed: 30423294
pmcid: 6280965
doi: 10.1016/j.ccell.2018.10.005
Roca-Portoles, A. et al. Venetoclax causes metabolic reprogramming independent of BCL-2 inhibition. Cell Death Dis. 11, 1–13 (2020).
doi: 10.1038/s41419-020-02867-2
Zhang, T. et al. Apolipoprotein C2 - CD36 promotes leukemia growth and presents a targetable axis in acute myeloid leukemia. Blood Cancer Discov. 1, 198–213 (2020).
pubmed: 32944714
pmcid: 7494214
doi: 10.1158/2643-3230.BCD-19-0077
Zhang, Y. et al. IL-6 promotes chemoresistance via upregulating CD36 mediated fatty acids uptake in acute myeloid leukemia. Exp. Cell Res. 415, 113112 (2022).
pubmed: 35346671
doi: 10.1016/j.yexcr.2022.113112
Mwaikambo, B. R., Sennlaub, F., Ong, H., Chemtob, S. & Hardy, P. Activation of CD36 inhibits and induces regression of inflammatory corneal neovascularization. Investig. Ophthalmol. Vis. Sci. 47, 4356–4364 (2006).
doi: 10.1167/iovs.05-1656
Pascual, G. et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541, 41–45 (2017).
pubmed: 27974793
doi: 10.1038/nature20791
Cheng, P.-L. et al. Chemoresistance in acute myeloid leukemia: an alternative single-cell RNA sequencing approach. Hematol. Oncol. https://doi.org/10.1002/hon.3129 (2023).
Li, K. et al. Single-cell analysis reveals the chemotherapy-induced cellular reprogramming and novel therapeutic targets in relapsed/refractory acute myeloid leukemia. Leukemia 37, 308–325 (2023).
pubmed: 36543880
doi: 10.1038/s41375-022-01789-6
Zhai, Y. et al. Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia. Mol. Cancer 21, 166 (2022).
pubmed: 35986270
pmcid: 9389773
doi: 10.1186/s12943-022-01635-4
Naldini, M. M. et al. Longitudinal single-cell profiling of chemotherapy response in acute myeloid leukemia. Nat. Commun. 14, 1285 (2023).
pubmed: 36890137
pmcid: 9995364
doi: 10.1038/s41467-023-36969-0
Behbehani, G. K. et al. Mass cytometric functional profiling of acute myeloid leukemia defines cell-cycle and immunophenotypic properties that correlate with known responses to therapy. Cancer Discov. 5, 988–1003 (2015).
pubmed: 26091827
pmcid: 4652947
doi: 10.1158/2159-8290.CD-15-0298
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
doi: 10.1016/j.cell.2015.05.047
Tislevoll, B. S. et al. Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Nat. Commun. 14, 1–17 (2023).
DiNardo, C. D. et al. Venetoclax combined with FLAG-IDA induction and consolidation in newly diagnosed and relapsed or refractory acute myeloid leukemia. J. Clin. Oncol. 39, 2768–2778 (2021).
pubmed: 34043428
pmcid: 8407653
doi: 10.1200/JCO.20.03736
Brancati, S. et al. Venetoclax in relapsed/refractory acute myeloid leukemia: are supporting evidences enough? Cancers 14, 22 2021).
Jamy, O. et al. Hypomethylating agent/venetoclax versus intensive chemotherapy in adults with relapsed or refractory acute myeloid leukaemia. Br. J. Haematol. 198, e35–e37 (2022).
pubmed: 35509246
doi: 10.1111/bjh.18229
Graveno, M. E. et al. Venetoclax in combination with hypomethylating agents or low dose cytarabine for relapsed and refractory acute myeloid leukemia. Leuk. Lymphoma 63, 1645–1650 (2022).
pubmed: 35259056
doi: 10.1080/10428194.2022.2042688
Lagadinou, E. D. et al. BCL-2 inhibition targets oxidative phosphorylation and selectively eradicates quiescent human leukemia stem cells. Cell Stem Cell 12, 329–341 (2013).
pubmed: 23333149
pmcid: 3595363
doi: 10.1016/j.stem.2012.12.013
Pollyea, D. A. et al. Venetoclax with azacitidine disrupts energy metabolism and targets leukemia stem cells in patients with acute myeloid leukemia. Nat. Med. 24, 1859–1866 (2018).
pubmed: 30420752
pmcid: 7001730
doi: 10.1038/s41591-018-0233-1
Guièze, R. et al. Mitochondrial reprogramming underlies resistance to BCL-2 inhibition in lymphoid malignancies. Cancer Cell 36, 369–384.e13 (2019).
pubmed: 31543463
pmcid: 6801112
doi: 10.1016/j.ccell.2019.08.005
Guerrero-Rodríguez, S. L., Mata-Cruz, C., Pérez-Tapia, S. M. & Velasco-Velázquez, M. A. Role of CD36 in cancer progression, stemness, and targeting. Front. Cell Dev. Biol. 10, 2362 (2022).
doi: 10.3389/fcell.2022.1079076
Dolgin, E. A drug to block fat intake and combat cancer spread. Nature Publishing Group UK https://doi.org/10.1038/d41586-021-01667-8 (2021).
Farge, T. et al. CD36 drives metastasis and relapse in acute myeloid leukemia. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-22-3682 (2023).
Chen, Y., Zhang, J., Cui, W. & Silverstein, R. L. CD36, a signaling receptor and fatty acid transporter that regulates immune cell metabolism and fate. J. Exp. Med. 219, eabo6891 (2022).
Elias, E. E. et al. Venetoclax-resistant CLL cells show a highly activated and proliferative phenotype. Cancer Immunol. Immunother. 71, 979–987 (2022).
pubmed: 34467417
doi: 10.1007/s00262-021-03043-x
Döhner, H. et al. Randomized, phase 2 trial of low-dose cytarabine with or without volasertib in AML patients not suitable for induction therapy. Blood 124, 1426–1433 (2014).
pubmed: 25006120
pmcid: 4148765
doi: 10.1182/blood-2014-03-560557
Platzbecker, U. et al. Volasertib as a monotherapy or in combination with azacitidine in patients with myelodysplastic syndrome, chronic myelomonocytic leukemia, or acute myeloid leukemia: summary of three phase I studies. BMC Cancer 22, 569 (2022).
pubmed: 35597904
pmcid: 9124414
doi: 10.1186/s12885-022-09622-0
Döhner, H. et al. Adjunctive volasertib in patients with acute myeloid leukemia not eligible for standard induction therapy: a randomized, phase 3 trial. Hemasphere 5, e617 (2021).
pubmed: 34350385
pmcid: 8328241
doi: 10.1097/HS9.0000000000000617
Severin, Y. et al. Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes. Sci. Adv. 8, eabn5631 (2022).
pubmed: 36322666
pmcid: 9629716
doi: 10.1126/sciadv.abn5631
Carpenter, A. E. et al. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
Vladimer, G. I. et al. Global survey of the immunomodulatory potential of common drugs. Nat. Chem. Biol. 13, 681–690 (2017).
pubmed: 28437395
pmcid: 5438060
doi: 10.1038/nchembio.2360
Bertolini, A. et al. scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics. PLoS Comput. Biol. 18, e1010097 (2022).
pubmed: 35658001
pmcid: 9200350
doi: 10.1371/journal.pcbi.1010097
Prummer, M. et al. scROSHI: robust supervised hierarchical identification of single cells. NAR Genom. Bioinform. 5, lqad058 (2023).
pubmed: 37332656
pmcid: 10273189
doi: 10.1093/nargab/lqad058
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
doi: 10.1016/j.cell.2021.04.048
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
pubmed: 25822800
pmcid: 4739640
doi: 10.1038/nmeth.3337
Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
pubmed: 27122128
doi: 10.1186/s13059-016-0947-7
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. https://doi.org/10.1038/nbt.4091 (2018).
Poličar, P. G., Stražar, M. & Zupan, B. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. J. Stat. Softw. 109, 1–30 (2019).
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S. & Kluger, Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat. Methods 16, 243–245 (2019).
pubmed: 30742040
pmcid: 6402590
doi: 10.1038/s41592-018-0308-4
Kuipers, J., Tuncel, M. A., Ferreira, P., Jahn, K. & Beerenwinkel, N. Single-cell copy number calling and event history reconstruction. bioRxiv 2020.04.28.065755 https://doi.org/10.1101/2020.04.28.065755 (2020).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Casanova, R. et al. Standardization of suspension and imaging mass cytometry readouts for clinical decision making. bioRxiv 2023.03.19.531228 https://doi.org/10.1101/2023.03.19.531228 (2023).
Crowell, H. L. et al. An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data. F1000Res 9, 1263 (2020).
pubmed: 36072920
doi: 10.12688/f1000research.26073.1
Xuan, Y. et al. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nat. Commun. 11, 5248 (2020).
pubmed: 33067419
pmcid: 7568553
doi: 10.1038/s41467-020-18904-9
Forny, P. et al. Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency. Nat. Metab. 5, 80–95 (2023).
pubmed: 36717752
pmcid: 9886552
doi: 10.1038/s42255-022-00720-8
Čuklina, J. et al. Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial. Mol. Syst. Biol. 17, e10240 (2021).
pubmed: 34432947
pmcid: 8447595
doi: 10.15252/msb.202110240
Kramer, B. A., Del Castillo, J. S., Pelkmans, L. & Gut, G. Iterative Indirect Immunofluorescence Imaging (4i) on Adherent Cells and Tissue Sections. Bio Protoc. 13, e4712 (2023).
pubmed: 37449033
pmcid: 10336569
doi: 10.21769/BioProtoc.4712
Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133 (2013).
pubmed: 24097269
doi: 10.1038/nmeth.2657
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Guangchuang Yu, Li-Gen Wang, Yanyan Han, and Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology (2012).
Szklarczyk, D. et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).
pubmed: 33237311
doi: 10.1093/nar/gkaa1074
Love, M. I., Anders, S. & Huber, W. Differential Analysis of Count Data - the DESeq2 Package. vol. 15 550 (2014).