Deciphering tumor microenvironment and immunotherapy targets in colorectal cancer by multi-omics

Colorectal cancer (CRC) is a major global health challenge, ranking as the third most common cancer worldwide and the second leading cause of cancer-related deaths, with approximately two million new cases and one million deaths related to CRC worldwide each year. The majority of CRC patients are in the middle or late stages at the time of initial diagnosis, and the prognosis of metastatic CRC is poor, with a 5-year survival rate of only about 14%. Immunotherapy, which activates the immune system to eliminate malignant cells, is one of the most promising therapeutic approaches. However, the efficacy of immunotherapy heavily depends on the tumor microenvironment (TME) of CRC, and the lack of understanding of the CRC TME has significantly hindered effective treatment. Therefore, deciphering the CRC tumor microenvironment and identifying new immunotherapeutic targets are crucial for improving patient survival rates.

We constructed a comprehensive cell atlas for CRC by integrating public datasets and self-produced datasets, comprising over a million single cells from various sources, including healthy colon tissue, normal, paracancerous, and tumor tissues. We found that myeloid cells significantly enriched in the tumor tissue by comparing the cell types composition in the cell atlas. The immune inhibitory scores of myeloid cells were the highest among all of the immune cells, indicating that tumor-associated myeloid cells may be the primary inducers of the immunosuppressive microenvironment within the tumor. Gene expression tendency analysis revealed that macrophages and neutrophils exhibited increasingly pro-tumorigenic characteristics as the tumor stage progresses. Besides, there are many specifically enhanced cell-cell interactions in the tumor tissue, such as the immunosuppressive Ligand-Receptor (L-R) pairs, suggesting that these highly expressed L-R pairs in tumor tissue could be a novel target for immunotherapy.

In-depth analysis revealed that tumor-associated macrophages (TAMs) and granulocytic myeloid-derived suppressor cells (gMDSCs) were significantly enriched in tumor tissue and exhibited the strongest immunosuppressive activity, highly expressing immunosuppressive receptors that contain immunoreceptor tyrosine-based inhibitory motif (ITIM), such as SIRPA. Intriguingly, Sirpa^-/- mice exhibited slower tumor progression and higher survival rates in subcutaneous MC38 model or AOM/DSS-induced CRC model than wild-type mice, indicating that Sirpα deficiency significantly inhibits tumor development. Moreover, the inhibition of tumor development by Sirpα deficiency was more pronounced than that of CD47 knockout and blockade, suggesting that Sirpα promotes tumor immune evasion independently of interaction with the ligand CD47. Further single-cell RNA sequencing (scRNA-seq) analysis of tumor tissues from AOM/DSS induced CRC model revealed that Sirpα deficiency led to expansion of TAM_Ccl8^hi and gMDSC_H2-Q10^hi subsets which exhibited enhanced antigen-presenting ability, phagocytic capacity, and inflammatory response. These subpopulations promoted the proliferation, activation, and recruitment of T cells, thereby shaping a tumor microenvironment with a stronger antitumor effect. Furthermore, Sirpa^-/- macrophages primarily facilitated T cell recruitment via Syk/Btk-dependent Ccl8 secretion to exert antitumor response. Importantly, we observed that the combination therapy of Sirpα knockout and anti-PD-L1 treatment exhibited enhanced tumor suppression effects, suggesting that Sirpα could be a promising novel target for immunotherapy of solid tumors.

To comprehensively understand the TME in CRC, we further integrated scRNA-seq and spatial transcriptomics data to reveal differences in spatial architecture and cellular distribution between normal and tumor tissues in CRC patients. In normal tissue, the structure was well-organized, displaying an intact smooth muscle cell (SMC) layer. In contrast, the tumor tissue exhibited a disorganized structure, with tumor connective tissue interwoven in a scattered pattern and lacking a formed SMC layer. Additionally, the digitalization analysis of the mucosal layers in normal tissues revealed that enterocyte progenitor cells exhibited predominantly enrichment in the junction of smooth muscle and mucosa (SMJ) layer, while mature enterocytes were clustered at the top of the mucosal layer (Mucosa VI). The spatial distribution from these progenitors to mature cells was consistent with the trajectory analysis results from scRNA-seq data. Besides, plasma cells were primarily distributed in the Mucosal IV and V layers, consistent with the gradient expression of PIGR, and IGHA1/2. This revealed the spatial location where the endocytosis transportation of immunoglobulins occurs in intestinal epithelial cells. Spatial colocalization and proximity analysis revealed that T cells and B cells closely colocalized in normal tissues, while T cells and dendritic cells (DCs), TAM and cancer-associated fibroblast (CAF) exhibited significant colocalization in tumor tissues. Furthermore, the spatial aggregation of different cell types in tumor tissues was consistent with the spatial expression of their specific L-R pairs, such as CCL19-CCR7 between DCs and T cells, SPP1-ITGA5/ITGB2 between macrophage and fibroblast, and PDCD1LG2-PDCD1 between macrophages and T cells. The spatial atlas unveils the intricate landscape within tumors, offering deep insights into the TME.

In summary, through constructing a large-scale CRC single-cell atlas, this study revealed multiple tumor-specific myeloid subpopulations exhibiting the strongest immunosuppression capability. These tumor-associated myeloid cells specifically expressed high level of the immunosuppressive receptors, such as SIRPA. Besides, we found that Sirpα deficiency enhanced both innate and adaptive immunity, and modulated anti-tumor immune microenvironment. Combining Sirpα knockout and anti-PD-L1 treatment significantly inhibited tumor growth based on mice tumor models, suggesting that Sirpα could be a promising target for immunotherapy. Furthermore, through integrating scRNA-seq and spatial transcriptomics data, we profiled spatially-resolved CRC tumor microenvironment and identified tumor-specific spatial molecules and L-R pairs. These findings provide crucial insights for exploring new therapeutic targets and improving the outcomes of CRC patients.

[1] DEKKER E, TANIS P J, VLEUGELS J L A, et al. Colorectal cancer [J]. Lancet, 2019, 394(10207): 1467-80.
[2] SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. Ca-a Cancer Journal for Clinicians, 2021, 71(3): 209-49.
[3] THE LANCET O. Colorectal cancer: a disease of the young? [J]. Lancet Oncol, 2017, 18(4): 413.
[4] ZHOU J C, ZHENG R S, ZHANG S W, et al. Colorectal cancer burden and trends: Comparison between China and major burden countries in the world [J]. Chinese Journal of Cancer Research, 2021, 33(1): 1-10.
[5] PATEL S G, KARLITZ J J, YEN T, et al. The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection [J]. Lancet Gastroenterol Hepatol, 2022, 7(3): 262-74.
[6] BARETTI M, LE D T. DNA mismatch repair in cancer [J]. Pharmacol Ther, 2018, 189: 45-62.
[7] KUNKEL T A, ERIE D A. Eukaryotic Mismatch Repair in Relation to DNA Replication [J]. Annu Rev Genet, 2015, 49: 291-313.
[8] RICHMAN S. Deficient mismatch repair: Read all about it (Review) [J]. Int J Oncol, 2015, 47(4): 1189-202.
[9] ELLEGREN H. Microsatellites: simple sequences with complex evolution [J]. Nature Reviews Genetics, 2004, 5(6): 435-45.
[10] GUINNEY J, DIENSTMANN R, WANG X, et al. The consensus molecular subtypes of colorectal cancer [J]. Nat Med, 2015, 21(11): 1350-6.
[11] JIN M-Z, JIN W-L. The updated landscape of tumor microenvironment and drug repurposing [J]. Signal Transduction and Targeted Therapy, 2020, 5(1): 166.
[12] BAGHBAN R, ROSHANGAR L, JAHANBAN-ESFAHLAN R, et al. Tumor microenvironment complexity and therapeutic implications at a glance [J]. Cell Commun Signal, 2020, 18(1): 59.
[13] BILLER L H, SCHRAG D. Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review [J]. Jama, 2021, 325(7): 669-85.
[14] GANESH K, STADLER Z K, CERCEK A, et al. Immunotherapy in colorectal cancer: rationale, challenges and potential [J]. Nature Reviews Gastroenterology & Hepatology, 2019, 16(6): 361-75.
[15] SHAN J, HAN D, SHEN C, et al. Mechanism and strategies of immunotherapy resistance in colorectal cancer [J]. Front Immunol, 2022, 13: 1016646.
[16] MöRBE U M, JøRGENSEN P B, FENTON T M, et al. Human gut-associated lymphoid tissues (GALT); diversity, structure, and function [J]. Mucosal Immunology, 2021, 14(4): 793-802.
[17] NELSON C M. The mechanics of crypt morphogenesis [J]. Nature Cell Biology, 2021, 23(7): 678-9.
[18] JOHANSSON M E, LARSSON J M, HANSSON G C. The two mucus layers of colon are organized by the MUC2 mucin, whereas the outer layer is a legislator of host-microbial interactions [J]. Proc Natl Acad Sci U S A, 2011, 108 Suppl 1(Suppl 1): 4659-65.
[19] GRIBBLE F M, REIMANN F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism [J]. Nature Reviews Endocrinology, 2019, 15(4): 226-37.
[20] CHENG H, LEBLOND C P. Origin, differentiation and renewal of the four main epithelial cell types in the mouse small intestine. V. Unitarian Theory of the origin of the four epithelial cell types [J]. Am J Anat, 1974, 141(4): 537-61.
[21] BARKER N, VAN ES J H, KUIPERS J, et al. Identification of stem cells in small intestine and colon by marker gene Lgr5 [J]. Nature, 2007, 449(7165): 1003-7.
[22] VAN DE WETERING M, SANCHO E, VERWEIJ C, et al. The beta-catenin/TCF-4 complex imposes a crypt progenitor phenotype on colorectal cancer cells [J]. Cell, 2002, 111(2): 241-50.
[23] BARKER N, CLEVERS H. Leucine-rich repeat-containing G-protein-coupled receptors as markers of adult stem cells [J]. Gastroenterology, 2010, 138(5): 1681-96.
[24] VAN DER FLIER L G, CLEVERS H. Stem cells, self-renewal, and differentiation in the intestinal epithelium [J]. Annu Rev Physiol, 2009, 71: 241-60.
[25] SANGIORGI E, CAPECCHI M R. Bmi1 is expressed in vivo in intestinal stem cells [J]. Nat Genet, 2008, 40(7): 915-20.
[26] ZHU L, GIBSON P, CURRLE D S, et al. Prominin 1 marks intestinal stem cells that are susceptible to neoplastic transformation [J]. Nature, 2009, 457(7229): 603-7.
[27] KAIKO G E, RYU S H, KOUES O I, et al. The Colonic Crypt Protects Stem Cells from Microbiota-Derived Metabolites [J]. Cell, 2016, 167(4): 1137.
[28] LIU J, WALKER N M, COOK M T, et al. Functional Cftr in crypt epithelium of organotypic enteroid cultures from murine small intestine [J]. Am J Physiol Cell Physiol, 2012, 302(10): C1492-503.
[29] TREZISE A E, ROMANO P R, GILL D R, et al. The multidrug resistance and cystic fibrosis genes have complementary patterns of epithelial expression [J]. EMBO J, 1992, 11(12): 4291-303.
[30] BIRCHENOUGH G M, NYSTROM E E, JOHANSSON M E, et al. A sentinel goblet cell guards the colonic crypt by triggering Nlrp6-dependent Muc2 secretion [J]. Science, 2016, 352(6293): 1535-42.
[31] PINTO D, GREGORIEFF A, BEGTHEL H, et al. Canonical Wnt signals are essential for homeostasis of the intestinal epithelium [J]. Genes Dev, 2003, 17(14): 1709-13.
[32] LIN G, XU N, XI R. Paracrine Wingless signalling controls self-renewal of Drosophila intestinal stem cells [J]. Nature, 2008, 455(7216): 1119-23.
[33] ZORN A M. Wnt signalling: antagonistic Dickkopfs [J]. Curr Biol, 2001, 11(15): R592-5.
[34] HE X C, ZHANG J, TONG W G, et al. BMP signaling inhibits intestinal stem cell self-renewal through suppression of Wnt-beta-catenin signaling [J]. Nat Genet, 2004, 36(10): 1117-21.
[35] HOWE J R, BAIR J L, SAYED M G, et al. Germline mutations of the gene encoding bone morphogenetic protein receptor 1A in juvenile polyposis [J]. Nature Genetics, 2001, 28(2): 184-7.
[36] SCOVILLE D H, SATO T, HE X C, et al. Current view: intestinal stem cells and signaling [J]. Gastroenterology, 2008, 134(3): 849-64.
[37] HARAMIS A P, BEGTHEL H, VAN DEN BORN M, et al. De novo crypt formation and juvenile polyposis on BMP inhibition in mouse intestine [J]. Science, 2004, 303(5664): 1684-6.
[38] BATTS L E, POLK D B, DUBOIS R N, et al. Bmp signaling is required for intestinal growth and morphogenesis [J]. Dev Dyn, 2006, 235(6): 1563-70.
[39] MARSHMAN E, BOOTH C, POTTEN C S. The intestinal epithelial stem cell [J]. Bioessays, 2002, 24(1): 91-8.
[40] OHLSTEIN B, SPRADLING A. Multipotent Drosophila intestinal stem cells specify daughter cell fates by differential notch signaling [J]. Science, 2007, 315(5814): 988-92.
[41] ARTAVANIS-TSAKONAS S, RAND M D, LAKE R J. Notch signaling: cell fate control and signal integration in development [J]. Science, 1999, 284(5415): 770-6.
[42] MILANO J, MCKAY J, DAGENAIS C, et al. Modulation of notch processing by gamma-secretase inhibitors causes intestinal goblet cell metaplasia and induction of genes known to specify gut secretory lineage differentiation [J]. Toxicol Sci, 2004, 82(1): 341-58.
[43] CROSNIER C, STAMATAKI D, LEWIS J. Organizing cell renewal in the intestine: stem cells, signals and combinatorial control [J]. Nature Reviews Genetics, 2006, 7(5): 349-59.
[44] TODARO M, FRANCIPANE M G, MEDEMA J P, et al. Colon cancer stem cells: promise of targeted therapy [J]. Gastroenterology, 2010, 138(6): 2151-62.
[45] SCHMITT M, GRETEN F R. The inflammatory pathogenesis of colorectal cancer [J]. Nat Rev Immunol, 2021, 21(10): 653-67.
[46] SONG C, CHAI Z, CHEN S, et al. Intestinal mucus components and secretion mechanisms: what we do and do not know [J]. Exp Mol Med, 2023, 55(4): 681-91.
[47] SHAN M, GENTILE M, YEISER J R, et al. Mucus enhances gut homeostasis and oral tolerance by delivering immunoregulatory signals [J]. Science, 2013, 342(6157): 447-53.
[48] MENG H, LI W, BOARDMAN L A, et al. Loss of ZG16 is associated with molecular and clinicopathological phenotypes of colorectal cancer [J]. BMC Cancer, 2018, 18(1): 433.
[49] MENG H, DING Y, LIU E, et al. ZG16 regulates PD-L1 expression and promotes local immunity in colon cancer [J]. Transl Oncol, 2021, 14(2): 101003.
[50] LI X, YANG Y, HUANG Q, et al. Crosstalk Between the Tumor Microenvironment and Cancer Cells: A Promising Predictive Biomarker for Immune Checkpoint Inhibitors [J]. Front Cell Dev Biol, 2021, 9: 738373.
[51] PAGE A, CHUVIN N, VALLADEAU-GUILEMOND J, et al. Development of NK cell-based cancer immunotherapies through receptor engineering [J]. Cellular & Molecular Immunology, 2024.
[52] DEL PRETE A, SALVI V, SORIANI A, et al. Dendritic cell subsets in cancer immunity and tumor antigen sensing [J]. Cellular & Molecular Immunology, 2023, 20(5): 432-47.
[53] WANG Y, XIANG Y, XIN V W, et al. Dendritic cell biology and its role in tumor immunotherapy [J]. Journal of Hematology & Oncology, 2020, 13(1): 107.
[54] CHRISTOFIDES A, STRAUSS L, YEO A, et al. The complex role of tumor-infiltrating macrophages [J]. Nature Immunology, 2022, 23(8): 1148-56.
[55] ÖHLUND D, HANDLY-SANTANA A, BIFFI G, et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer [J]. J Exp Med, 2017, 214(3): 579-96.
[56] PENG Z, YE M, DING H, et al. Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer [J]. J Transl Med, 2022, 20(1): 302.
[57] ELYADA E, BOLISETTY M, LAISE P, et al. Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts [J]. Cancer Discov, 2019, 9(8): 1102-23.
[58] KALLURI R, ZEISBERG M. Fibroblasts in cancer [J]. Nat Rev Cancer, 2006, 6(5): 392-401.
[59] PIERA-VELAZQUEZ S, JIMENEZ S A. Endothelial to Mesenchymal Transition: Role in Physiology and in the Pathogenesis of Human Diseases [J]. Physiol Rev, 2019, 99(2): 1281-324.
[60] BOCHET L, LEHUéDé C, DAUVILLIER S, et al. Adipocyte-derived fibroblasts promote tumor progression and contribute to the desmoplastic reaction in breast cancer [J]. Cancer Res, 2013, 73(18): 5657-68.
[61] NIKOLIC-PATERSON D J, WANG S, LAN H Y. Macrophages promote renal fibrosis through direct and indirect mechanisms [J]. Kidney Int Suppl (2011), 2014, 4(1): 34-8.
[62] LAGORY E L, GIACCIA A J. The ever-expanding role of HIF in tumour and stromal biology [J]. Nat Cell Biol, 2016, 18(4): 356-65.
[63] PALAZON A, TYRAKIS P A, MACIAS D, et al. An HIF-1α/VEGF-A Axis in Cytotoxic T Cells Regulates Tumor Progression [J]. Cancer Cell, 2017, 32(5): 669-83.e5.
[64] FUKUMURA D, XU L, CHEN Y, et al. Hypoxia and acidosis independently up-regulate vascular endothelial growth factor transcription in brain tumors in vivo [J]. Cancer Res, 2001, 61(16): 6020-4.
[65] BOEDTKJER E, PEDERSEN S F. The Acidic Tumor Microenvironment as a Driver of Cancer [J]. Annu Rev Physiol, 2020, 82: 103-26.
[66] ZHAO H, WU L, YAN G, et al. Inflammation and tumor progression: signaling pathways and targeted intervention [J]. Signal Transduction and Targeted Therapy, 2021, 6(1): 263.
[67] IVASHKIV L B. IFNγ: signalling, epigenetics and roles in immunity, metabolism, disease and cancer immunotherapy [J]. Nature Reviews Immunology, 2018, 18(9): 545-58.
[68] SARAIVA M, O'GARRA A. The regulation of IL-10 production by immune cells [J]. Nature Reviews Immunology, 2010, 10(3): 170-81.
[69] WU Y, ZHOU B P. TNF-α/NF-κB/Snail pathway in cancer cell migration and invasion [J]. British Journal of Cancer, 2010, 102(4): 639-44.
[70] LIU S, REN J, TEN DIJKE P. Targeting TGFβ signal transduction for cancer therapy [J]. Signal Transduction and Targeted Therapy, 2021, 6(1): 8.
[71] MILLER M C, MAYO K H. Chemokines from a Structural Perspective [J]. Int J Mol Sci, 2017, 18(10).
[72] BACHELERIE F, BEN-BARUCH A, BURKHARDT A M, et al. International Union of Basic and Clinical Pharmacology. [corrected]. LXXXIX. Update on the extended family of chemokine receptors and introducing a new nomenclature for atypical chemokine receptors [J]. Pharmacol Rev, 2014, 66(1): 1-79.
[73] MEMPEL T R, LILL J K, ALTENBURGER L M. How chemokines organize the tumour microenvironment [J]. Nature Reviews Cancer, 2024, 24(1): 28-50.
[74] MARIANI M, LANG R, BINDA E, et al. Dominance of CCL22 over CCL17 in induction of chemokine receptor CCR4 desensitization and internalization on human Th2 cells [J]. Eur J Immunol, 2004, 34(1): 231-40.
[75] ROUSSOS E T, CONDEELIS J S, PATSIALOU A. Chemotaxis in cancer [J]. Nature Reviews Cancer, 2011, 11(8): 573-87.
[76] FARES J, FARES M Y, KHACHFE H H, et al. Molecular principles of metastasis: a hallmark of cancer revisited [J]. Signal Transduct Target Ther, 2020, 5(1): 28.
[77] CHOW M T, OZGA A J, SERVIS R L, et al. Intratumoral Activity of the CXCR3 Chemokine System Is Required for the Efficacy of Anti-PD-1 Therapy [J]. Immunity, 2019, 50(6): 1498-512.e5.
[78] MIKUCKI M E, FISHER D T, MATSUZAKI J, et al. Non-redundant requirement for CXCR3 signalling during tumoricidal T-cell trafficking across tumour vascular checkpoints [J]. Nat Commun, 2015, 6: 7458.
[79] ZHANG Y, ZHANG Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications [J]. Cellular & Molecular Immunology, 2020, 17(8): 807-21.
[80] MOROTTI M, ALBUKHARI A, ALSAADI A, et al. Promises and challenges of adoptive T-cell therapies for solid tumours [J]. British Journal of Cancer, 2021, 124(11): 1759-76.
[81] ZHANG P, ZHANG G, WAN X. Challenges and new technologies in adoptive cell therapy [J]. Journal of Hematology & Oncology, 2023, 16(1): 97.
[82] ROZENBLIT M, HUANG R, DANZIGER N, et al. Comparison of PD-L1 protein expression between primary tumors and metastatic lesions in triple negative breast cancers [J]. J Immunother Cancer, 2020, 8(2).
[83] DUFFY M J, CROWN J. Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients [J]. Clin Chem, 2019, 65(10): 1228-38.
[84] ZHANG Z, LIU X, CHEN D, et al. Radiotherapy combined with immunotherapy: the dawn of cancer treatment [J]. Signal Transduction and Targeted Therapy, 2022, 7(1): 258.
[85] ELBANNA M, CHOWDHURY N N, RHOME R, et al. Clinical and Preclinical Outcomes of Combining Targeted Therapy With Radiotherapy [J]. Front Oncol, 2021, 11: 749496.
[86] TANG F, BARBACIORU C, WANG Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell [J]. Nat Methods, 2009, 6(5): 377-82.
[87] ISLAM S, KJäLLQUIST U, MOLINER A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq [J]. Genome Res, 2011, 21(7): 1160-7.
[88] WEN L, TANG F. Single-cell sequencing in stem cell biology [J]. Genome Biol, 2016, 17: 71.
[89] HAN X, WANG R, ZHOU Y, et al. Mapping the Mouse Cell Atlas by Microwell-Seq [J]. Cell, 2018, 172(5): 1091-107.e17.
[90] FAN X, ZHANG X, WU X, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos [J]. Genome Biol, 2015, 16(1): 148.
[91] ZILIONIS R, NAINYS J, VERES A, et al. Single-cell barcoding and sequencing using droplet microfluidics [J]. Nat Protoc, 2017, 12(1): 44-73.
[92] MACOSKO E Z, BASU A, SATIJA R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets [J]. Cell, 2015, 161(5): 1202-14.
[93] CAO J, PACKER J S, RAMANI V, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism [J]. Science, 2017, 357(6352): 661-7.
[94] ROSENBERG A B, ROCO C M, MUSCAT R A, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding [J]. Science, 2018, 360(6385): 176-82.
[95] SRIVATSAN S R, MCFALINE-FIGUEROA J L, RAMANI V, et al. Massively multiplex chemical transcriptomics at single-cell resolution [J]. Science, 2020, 367(6473): 45-51.
[96] ZHENG G X, TERRY J M, BELGRADER P, et al. Massively parallel digital transcriptional profiling of single cells [J]. Nat Commun, 2017, 8: 14049.
[97] CHEN A, LIAO S, CHENG M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays [J]. Cell, 2022, 185(10): 1777-92.e21.
[98] MA S, ZHANG B, LAFAVE L M, et al. Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin [J]. Cell, 2020, 183(4): 1103-16 e20.
[99] XU W, YANG W, ZHANG Y, et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells [J]. Nature Methods, 2022, 19(10): 1243-9.
[100] CHEN A F, PARKS B, KATHIRIA A S, et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells [J]. Nat Methods, 2022, 19(5): 547-53.
[101] FISKIN E, LAREAU C A, LUDWIG L S, et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC [J]. Nature Biotechnology, 2022, 40(3): 374-81.
[102] LIU Y, DISTASIO M, SU G, et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq [J]. Nature Biotechnology, 2023.
[103] DEY S S, KESTER L, SPANJAARD B, et al. Integrated genome and transcriptome sequencing of the same cell [J]. Nature Biotechnology, 2015, 33(3): 285-9.
[104] DEY S S, KESTER L, SPANJAARD B, et al. Integrated genome and transcriptome sequencing of the same cell [J]. Nat Biotechnol, 2015, 33(3): 285-9.
[105] MACAULAY I C, HAERTY W, KUMAR P, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes [J]. Nat Methods, 2015, 12(6): 519-22.
[106] HAN K Y, KIM K T, JOUNG J G, et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells [J]. Genome Res, 2018, 28(1): 75-87.
[107] RODRIGUEZ-MEIRA A, BUCK G, CLARK S A, et al. Unravelling Intratumoral Heterogeneity through High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing [J]. Mol Cell, 2019, 73(6): 1292-305.e8.
[108] SMALLWOOD S A, LEE H J, ANGERMUELLER C, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity [J]. Nature Methods, 2014, 11(8): 817-20.
[109] GUO H S, ZHU P, WU X L, et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing [J]. Genome Research, 2013, 23(12): 2126-35.
[110] JIN W, TANG Q, WAN M, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples [J]. Nature, 2015, 528(7580): 142-6.
[111] BUENOSTRO J D, WU B J, LITZENBURGER U M, et al. Single-cell chromatin accessibility reveals principles of regulatory variation [J]. Nature, 2015, 523(7561): 486-U264.
[112] XU W, WEN Y, LIANG Y, et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility [J]. Nat Protoc, 2021, 16(8): 4084-107.
[113] CHEN X, MIRAGAIA R J, NATARAJAN K N, et al. A rapid and robust method for single cell chromatin accessibility profiling [J]. Nat Commun, 2018, 9(1): 5345.
[114] LAI B, GAO W, CUI K, et al. Principles of nucleosome organization revealed by single-cell micrococcal nuclease sequencing [J]. Nature, 2018, 562(7726): 281-5.
[115] ANGERMUELLER C, CLARK S J, LEE H J, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity [J]. Nat Methods, 2016, 13(3): 229-32.
[116] HU Y J, HUANG K, AN Q, et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell [J]. Genome Biology, 2016, 17.
[117] HOU Y, GUO H H, CAO C, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas [J]. Cell Res, 2016, 26(3): 304-19.
[118] CAO J Y, CUSANOVICH D A, RAMANI V, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells [J]. Science, 2018, 361(6409): 1380-5.
[119] LIU L Q, LIU C Y, QUINTERO A, et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity [J]. Nature Communications, 2019, 10.
[120] CHEN S, LAKE B B, ZHANG K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell [J]. Nat Biotechnol, 2019, 37(12): 1452-7.
[121] PLONGTHONGKUM N, DIEP D, CHEN S, et al. Scalable dual-omics profiling with single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2) [J]. Nat Protoc, 2021, 16(11): 4992-5029.
[122] ZHU C, YU M, HUANG H, et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome [J]. Nat Struct Mol Biol, 2019, 26(11): 1063-70.
[123] RANG F J, DE LUCA K L, DE VRIES S S, et al. Single-cell profiling of transcriptome and histone modifications with EpiDamID [J]. Mol Cell, 2022, 82(10): 1956-70 e14.
[124] LI G, LIU Y, ZHANG Y, et al. Joint profiling of DNA methylation and chromatin architecture in single cells [J]. Nat Methods, 2019, 16(10): 991-3.
[125] LEE D S, LUO C, ZHOU J, et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells [J]. Nat Methods, 2019, 16(10): 999-1006.
[126] GUO F, LI L, LI J Y, et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells [J]. Cell Res, 2017, 27(8): 967-88.
[127] CLARK S J, ARGELAGUET R, KAPOURANI C A, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells [J]. Nature Communications, 2018, 9.
[128] WANG Y, YUAN P, YAN Z, et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos [J]. Nature Communications, 2021, 12(1): 1247.
[129] YAN R, GU C, YOU D, et al. Decoding dynamic epigenetic landscapes in human oocytes using single-cell multi-omics sequencing [J]. Cell Stem Cell, 2021, 28(9): 1641-56 e7.
[130] GENSHAFT A S, LI S, GALLANT C J, et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction [J]. Genome Biology, 2016, 17.
[131] FREI A P, BAVA F A, ZUNDER E R, et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells [J]. Nat Methods, 2016, 13(3): 269-75.
[132] STOECKIUS M, HAFEMEISTER C, STEPHENSON W, et al. Simultaneous epitope and transcriptome measurement in single cells [J]. Nat Methods, 2017, 14(9): 865-8.
[133] PETERSON V M, ZHANG K X, KUMAR N, et al. Multiplexed quantification of proteins and transcripts in single cells [J]. Nat Biotechnol, 2017, 35(10): 936-9.
[134] MIMITOU E P, CHENG A, MONTALBANO A, et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells [J]. Nat Methods, 2019, 16(5): 409-12.
[135] FRANGIEH C J, MELMS J C, THAKORE P I, et al. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion [J]. Nat Genet, 2021, 53(3): 332-41.
[136] GERLACH J P, VAN BUGGENUM J A G, TANIS S E J, et al. Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells [J]. Sci Rep-Uk, 2019, 9.
[137] RIVELLO F, VAN BUIJTENEN E, MATUŁA K, et al. Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics [J]. Cell Rep Methods, 2021, 1(5): 100070.
[138] KATZENELENBOGEN Y, SHEBAN F, YALIN A, et al. Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer [J]. Cell, 2020, 182(4): 872-85.e19.
[139] CHUNG H, PARKHURST C N, MAGEE E M, et al. Joint single-cell measurements of nuclear proteins and RNA in vivo [J]. Nat Methods, 2021, 18(10): 1204-12.
[140] SWANSON E, LORD C, READING J, et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq [J]. Elife, 2021, 10.
[141] RUFF D W, DHINGRA D M, THOMPSON K, et al. High-Throughput Multimodal Single-Cell Targeted DNA and Surface Protein Analysis Using the Mission Bio Tapestri Platform [J]. Methods Mol Biol, 2022, 2386: 171-88.
[142] MIMITOU E P, LAREAU C A, CHEN K Y, et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells [J]. Nat Biotechnol, 2021, 39(10): 1246-58.
[143] RAJ A, VAN DEN BOGAARD P, RIFKIN S A, et al. Imaging individual mRNA molecules using multiple singly labeled probes [J]. Nature Methods, 2008, 5(10): 877-9.
[144] BATTICH N, STOEGER T, PELKMANS L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution [J]. Nature Methods, 2013, 10(11): 1127-33.
[145] LUBECK E, COSKUN A F, ZHIYENTAYEV T, et al. Single-cell in situ RNA profiling by sequential hybridization [J]. Nature Methods, 2014, 11(4): 360-1.
[146] SHAH S, LUBECK E, ZHOU W, et al. In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus [J]. Neuron, 2016, 92(2): 342-57.
[147] MOFFITT J R, HAO J J, WANG G P, et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization [J]. P Natl Acad Sci USA, 2016, 113(39): 11046-51.
[148] CHEN K H, BOETTIGER A N, MOFFITT J R, et al. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells [J]. Science, 2015, 348(6233): aaa6090.
[149] HAIMOVICH G, GERST J E. Single-molecule Fluorescence in situ Hybridization (smFISH) for RNA Detection in Adherent Animal Cells [J]. Bio Protoc, 2018, 8(21): e3070.
[150] CODELUPPI S, BORM L E, ZEISEL A, et al. Spatial organization of the somatosensory cortex revealed by osmFISH [J]. Nat Methods, 2018, 15(11): 932-5.
[151] WANG X, ALLEN W E, WRIGHT M A, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states [J]. Science, 2018, 361(6400).
[152] RODRIQUES S G, STICKELS R R, GOEVA A, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution [J]. Science, 2019, 363(6434): 1463-7.
[153] VICKOVIC S, ERASLAN G, SALMéN F, et al. High-definition spatial transcriptomics for in situ tissue profiling [J]. Nat Methods, 2019, 16(10): 987-90.
[154] LIU Y, YANG M, DENG Y, et al. High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue [J]. Cell, 2020, 183(6): 1665-81 e18.
[155] VICKOVIC S, LöTSTEDT B, KLUGHAMMER J, et al. SM-Omics is an automated platform for high-throughput spatial multi-omics [J]. Nature Communications, 2022, 13(1): 795.
[156] GUI G, WONG-ROLLE A, DILLON L W, et al. Spatial-Temporal Multiomic Analysis of Tumor-Immune Interactions in Patients with AML Receiving Pembrolizumab and Decitabine [J]. Blood, 2022, 140: 3427-8.
[157] MAYNARD A, MCCOACH C E, ROTOW J K, et al. Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing [J]. Cell, 2020, 182(5): 1232-51 e22.
[158] PURAM S V, TIROSH I, PARIKH A S, et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer [J]. Cell, 2017, 171(7): 1611-24 e24.
[159] VENTEICHER A S, TIROSH I, HEBERT C, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq [J]. Science, 2017, 355(6332).
[160] PATEL A P, TIROSH I, TROMBETTA J J, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma [J]. Science, 2014, 344(6190): 1396-401.
[161] QIN P, PANG Y, HOU W, et al. Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication [J]. Cell Discov, 2021, 7(1): 2.
[162] FU R, QIN P, ZOU X, et al. A Comprehensive Characterization of Monoallelic Expression During Hematopoiesis and Leukemogenesis via Single-Cell RNA-Sequencing [J]. Front Cell Dev Biol, 2021, 9: 702897.
[163] FRANZEN O, GAN L M, BJORKEGREN J L M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data [J]. Database (Oxford), 2019, 2019.
[164] CAO Y, ZHU J, JIA P, et al. scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells [J]. Genes (Basel), 2017, 8(12): 368.
[165] ABUGESSAISA I, NOGUCHI S, BOTTCHER M, et al. SCPortalen: human and mouse single-cell centric database [J]. Nucleic Acids Res, 2018, 46(D1): D781-D7.
[166] MORENO P, FEXOVA S, GEORGE N, et al. Expression Atlas update: gene and protein expression in multiple species [J]. Nucleic Acids Res, 2021.
[167] YUAN H, YAN M, ZHANG G, et al. CancerSEA: a cancer single-cell state atlas [J]. Nucleic Acids Res, 2019, 47(D1): D900-D8.
[168] FAN Z, CHEN R, CHEN X. SpatialDB: a database for spatially resolved transcriptomes [J]. Nucleic Acids Res, 2020, 48(D1): D233-D7.
[169] ZHANG K, HOCKER J D, MILLER M, et al. A single-cell atlas of chromatin accessibility in the human genome [J]. Cell, 2021, 184(24): 5985-6001 e19.
[170] DOMCKE S, HILL A J, DAZA R M, et al. A human cell atlas of fetal chromatin accessibility [J]. Science, 2020, 370(6518).
[171] REGEV A, TEICHMANN S A, LANDER E S, et al. The Human Cell Atlas [J]. Elife, 2017, 6: e27041.
[172] STUART T, BUTLER A, HOFFMAN P, et al. Comprehensive Integration of Single-Cell Data [J]. Cell, 2019, 177(7): 1888-902 e21.
[173] ARGELAGUET R, VELTEN B, ARNOL D, et al. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets [J]. Mol Syst Biol, 2018, 14(6): e8124.
[174] GAYOSO A, STEIER Z, LOPEZ R, et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI [J]. Nat Methods, 2021, 18(3): 272-82.
[175] LAKKIS J, SCHROEDER A, SU K, et al. A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation [J]. Nature Machine Intelligence, 2022, 4(11): 940-52.
[176] CAO Z J, GAO G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding [J]. Nat Biotechnol, 2022, 40(10): 1458-66.
[177] KANG J B, NATHAN A, WEINAND K, et al. Efficient and precise single-cell reference atlas mapping with Symphony [J]. Nat Commun, 2021, 12(1): 5890.
[178] KLESHCHEVNIKOV V, SHMATKO A, DANN E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics [J]. Nature Biotechnology, 2022, 40(5): 661-71.
[179] CABLE D M, MURRAY E, ZOU L S, et al. Robust decomposition of cell type mixtures in spatial transcriptomics [J]. Nat Biotechnol, 2022, 40(4): 517-26.
[180] ARGELAGUET R, CUOMO A S E, STEGLE O, et al. Computational principles and challenges in single-cell data integration [J]. Nature Biotechnology, 2021, 39(10): 1202-15.
[181] TARAZONA S, ARZALLUZ-LUQUE A, CONESA A. Undisclosed, unmet and neglected challenges in multi-omics studies [J]. Nature Computational Science, 2021, 1(6): 395-402.
[182] THORSSON V, GIBBS D L, BROWN S D, et al. The Immune Landscape of Cancer [J]. Immunity, 2018, 48(4): 812-30.e14.
[183] HINSHAW D C, SHEVDE L A. The Tumor Microenvironment Innately Modulates Cancer Progression [J]. Cancer Res, 2019, 79(18): 4557-66.
[184] KOLIARAKI V, PRADOS A, ARMAKA M, et al. The mesenchymal context in inflammation, immunity and cancer [J]. Nat Immunol, 2020, 21(9): 974-82.
[185] GALIPEAU J, SENSéBé L. Mesenchymal Stromal Cells: Clinical Challenges and Therapeutic Opportunities [J]. Cell Stem Cell, 2018, 22(6): 824-33.
[186] JALKANEN S, SALMI M. Lymphatic endothelial cells of the lymph node [J]. Nat Rev Immunol, 2020, 20(9): 566-78.
[187] AMERSFOORT J, EELEN G, CARMELIET P. Immunomodulation by endothelial cells - partnering up with the immune system? [J]. Nat Rev Immunol, 2022: 1-13.
[188] CARDENAS M A, PROKHNEVSKA N, KISSICK H T. Organized immune cell interactions within tumors sustain a productive T-cell response [J]. Int Immunol, 2021, 33(1): 27-37.
[189] SALTZ J, GUPTA R, HOU L, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images [J]. Cell Rep, 2018, 23(1): 181-93.e7.
[190] BINNEWIES M, ROBERTS E W, KERSTEN K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy [J]. Nat Med, 2018, 24(5): 541-50.
[191] BEJARANO L, JORDĀO M J C, JOYCE J A. Therapeutic Targeting of the Tumor Microenvironment [J]. Cancer Discov, 2021, 11(4): 933-59.
[192] DEBERARDINIS R J. Tumor Microenvironment, Metabolism, and Immunotherapy [J]. N Engl J Med, 2020, 382(9): 869-71.
[193] WANG Z, GERSTEIN M, SNYDER M. RNA-Seq: a revolutionary tool for transcriptomics [J]. Nat Rev Genet, 2009, 10(1): 57-63.
[194] OZSOLAK F, MILOS P M. RNA sequencing: advances, challenges and opportunities [J]. Nat Rev Genet, 2011, 12(2): 87-98.
[195] ELMENTAITE R, KUMASAKA N, ROBERTS K, et al. Cells of the human intestinal tract mapped across space and time [J]. Nature, 2021, 597(7875): 250-5.
[196] ELMENTAITE R, ROSS A D B, ROBERTS K, et al. Single-Cell Sequencing of Developing Human Gut Reveals Transcriptional Links to Childhood Crohn's Disease [J]. Developmental Cell, 2020, 55(6): 771-83.
[197] FAWKNER-CORBETT D, ANTANAVICIUTE A, PARIKH K, et al. Spatiotemporal analysis of human intestinal development at single-cell resolution [J]. Cell, 2021, 184(3): 810-26.
[198] LI H, COURTOIS E T, SENGUPTA D, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors [J]. Nature Genetics, 2017, 49(5): 708-18.
[199] BIAN S, HOU Y, ZHOU X, et al. Single-cell multiomics sequencing and analyses of human colorectal cancer [J]. Science, 2018, 362(6418): 1060-3.
[200] ZHANG L, YU X, ZHENG L, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer [J]. Nature, 2018, 564(7735): 268-72.
[201] ZHANG L, LI Z, SKRZYPCZYNSKA K M, et al. Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer [J]. Cell, 2020, 181(2): 442-59 e29.
[202] LEE H O, HONG Y, ETLIOGLU H E, et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer [J]. Nat Genet, 2020, 52(6): 594-603.
[203] CHEN B, SCURRAH C R, MCKINLEY E T, et al. Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps [J]. Cell, 2021, 184(26): 6262-80.e26.
[204] GUO W, ZHANG C, WANG X, et al. Resolving the difference between left-sided and right-sided colorectal cancer by single-cell sequencing [J]. JCI Insight, 2022, 7(1).
[205] RAO A, BARKLEY D, FRANçA G S, et al. Exploring tissue architecture using spatial transcriptomics [J]. Nature, 2021, 596(7871): 211-20.
[206] LONGO S K, GUO M G, JI A L, et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics [J]. Nat Rev Genet, 2021, 22(10): 627-44.
[207] STåHL P L, SALMéN F, VICKOVIC S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics [J]. Science, 2016, 353(6294): 78-82.
[208] MERRITT C R, ONG G T, CHURCH S E, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue [J]. Nat Biotechnol, 2020, 38(5): 586-99.
[209] STICKELS R R, MURRAY E, KUMAR P, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 [J]. Nat Biotechnol, 2021, 39(3): 313-9.
[210] PELKA K, HOFREE M, CHEN J H, et al. Spatially organized multicellular immune hubs in human colorectal cancer [J]. Cell, 2021, 184(18): 4734-52.e20.
[211] WU Y, YANG S, MA J, et al. Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level [J]. Cancer Discov, 2022, 12(1): 134-53.
[212] QI J, SUN H, ZHANG Y, et al. Single-cell and spatial analysis reveal interaction of FAP(+) fibroblasts and SPP1(+) macrophages in colorectal cancer [J]. Nat Commun, 2022, 13(1): 1742.
[213] ZHONG C, WANG L, HU S, et al. Poly(I:C) enhances the efficacy of phagocytosis checkpoint blockade immunotherapy by inducing IL-6 production [J]. J Leukoc Biol, 2021, 110(6): 1197-208.
[214] SNIDER A J, BIALKOWSKA A B, GHALEB A M, et al. Murine Model for Colitis-Associated Cancer of the Colon [J]. Methods Mol Biol, 2016, 1438: 245-54.
[215] GRIVENNIKOV S, KARIN E, TERZIC J, et al. IL-6 and Stat3 are required for survival of intestinal epithelial cells and development of colitis-associated cancer [J]. Cancer Cell, 2009, 15(2): 103-13.
[216] WOLOCK S L, LOPEZ R, KLEIN A M. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data [J]. Cell Syst, 2019, 8(4): 281-91 e9.
[217] PARIKH K, ANTANAVICIUTE A, FAWKNER-CORBETT D, et al. Colonic epithelial cell diversity in health and inflammatory bowel disease [J]. Nature, 2019, 567(7746): 49-55.
[218] JOANITO I, WIRAPATI P, ZHAO N, et al. Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer [J]. Nat Genet, 2022, 54(7): 963-75.
[219] LOPEZ R, REGIER J, COLE M B, et al. Deep generative modeling for single-cell transcriptomics [J]. Nature Methods, 2018, 15(12): 1053-8.
[220] XU C, LOPEZ R, MEHLMAN E, et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models [J]. Mol Syst Biol, 2021, 17(1): e9620.
[221] LUECKEN M D, THEIS F J. Current best practices in single‐cell RNA‐seq analysis: a tutorial [J]. Molecular Systems Biology, 2019, 15(6): e8746.
[222] MAĆKIEWICZ A, RATAJCZAK W. Principal components analysis (PCA) [J]. Computers & Geosciences, 1993, 19(3): 303-42.
[223] VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE [J]. Journal of machine learning research, 2008, 9(11).
[224] BECHT E, MCINNES L, HEALY J, et al. Dimensionality reduction for visualizing single-cell data using UMAP [J]. Nat Biotechnol, 2018.
[225] LINDERMAN G C, RACHH M, HOSKINS J G, et al. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data [J]. Nat Methods, 2019, 16(3): 243-5.
[226] KOBAK D, BERENS P. The art of using t-SNE for single-cell transcriptomics [J]. Nat Commun, 2019, 10(1): 5416.
[227] CAO Y, LIN Y, ORMEROD J T, et al. scDC: single cell differential composition analysis [J]. BMC Bioinformatics, 2019, 20(Suppl 19): 721.
[228] BEZDEK J C, EHRLICH R, FULL W. FCM: The fuzzy c-means clustering algorithm [J]. Computers & Geosciences, 1984, 10(2): 191-203.
[229] YAARI G, BOLEN C R, THAKAR J, et al. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations [J]. Nucleic Acids Res, 2013, 41(18): e170.
[230] ZHOU Y, ZHOU B, PACHE L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets [J]. Nat Commun, 2019, 10(1): 1523.
[231] LA MANNO G, SOLDATOV R, ZEISEL A, et al. RNA velocity of single cells [J]. Nature, 2018, 560(7719): 494-8.
[232] BERGEN V, LANGE M, PEIDLI S, et al. Generalizing RNA velocity to transient cell states through dynamical modeling [J]. Nat Biotechnol, 2020, 38(12): 1408-14.
[233] LANGE M, BERGEN V, KLEIN M, et al. CellRank for directed single-cell fate mapping [J]. Nat Methods, 2022, 19(2): 159-70.
[234] JIN S, GUERRERO-JUAREZ C F, ZHANG L, et al. Inference and analysis of cell-cell communication using CellChat [J]. Nat Commun, 2021, 12(1): 1088.
[235] EFREMOVA M, VENTO-TORMO M, TEICHMANN S A, et al. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes [J]. Nat Protoc, 2020, 15(4): 1484-506.
[236] FANG S, XU M, CAO L, et al. Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics [J]. BioRxiv, 2023, 10.1101/2023.12.04.569485.
[237] WOLF F A, ANGERER P, THEIS F J. SCANPY: large-scale single-cell gene expression data analysis [J]. Genome Biol, 2018, 19(1): 15.
[238] BLONDEL V D, GUILLAUME J-L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks [J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008.
[239] TRAAG V A, WALTMAN L, VAN ECK N J. From Louvain to Leiden: guaranteeing well-connected communities [J]. Sci Rep, 2019, 9(1): 5233.
[240] QIU X, ZHU D Y, YAO J, et al. Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics [J]. BioRxiv, 2022, 10.1101/2022.12.07.519417.
[241] DOBIN A, DAVIS C A, SCHLESINGER F, et al. STAR: ultrafast universal RNA-seq aligner [J]. Bioinformatics, 2013, 29(1): 15-21.
[242] LI H, HANDSAKER B, WYSOKER A, et al. The Sequence Alignment/Map format and SAMtools [J]. Bioinformatics, 2009, 25(16): 2078-9.
[243] LIAO Y, SMYTH G K, SHI W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features [J]. Bioinformatics, 2014, 30(7): 923-30.
[244] LOVE M I, HUBER W, ANDERS S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 [J]. Genome Biol, 2014, 15(12): 550.
[245] MEI Y, LIU Y B, CAO S, et al. RIF1 promotes tumor growth and cancer stem cell-like traits in NSCLC by protein phosphatase 1-mediated activation of Wnt/β-catenin signaling [J]. Cell Death Dis, 2018, 9(10): 942.
[246] SUN B, ZHONG F-J, XU C, et al. Programmed cell death 10 promotes metastasis and epithelial-mesenchymal transition of hepatocellular carcinoma via PP2Ac-mediated YAP activation [J]. Cell Death & Disease, 2021, 12(9): 849.
[247] YAMAZAKI C, SUGIYAMA M, OHTA T, et al. Critical roles of a dendritic cell subset expressing a chemokine receptor, XCR1 [J]. J Immunol, 2013, 190(12): 6071-82.
[248] OHTA T, SUGIYAMA M, HEMMI H, et al. Crucial roles of XCR1-expressing dendritic cells and the XCR1-XCL1 chemokine axis in intestinal immune homeostasis [J]. Scientific Reports, 2016, 6(1): 23505.
[249] FöRSTER R, DAVALOS-MISSLITZ A C, ROT A. CCR7 and its ligands: balancing immunity and tolerance [J]. Nature Reviews Immunology, 2008, 8(5): 362-71.
[250] MORENO AYALA M A, CAMPBELL T F, ZHANG C, et al. CXCR3 expression in regulatory T cells drives interactions with type I dendritic cells in tumors to restrict CD8+ T cell antitumor immunity [J]. Immunity, 2023, 56(7): 1613-30.e5.
[251] SCHUMACHER T N, THOMMEN D S. Tertiary lymphoid structures in cancer [J]. Science, 2022, 375(6576): eabf9419.
[252] WHELAN S, OPHIR E, KOTTURI M F, et al. PVRIG and PVRL2 Are Induced in Cancer and Inhibit CD8(+) T-cell Function [J]. Cancer Immunol Res, 2019, 7(2): 257-68.
[253] ZHANG Q Q, ZHOU D L, LEI Y, et al. Slit2/Robo1 signaling promotes intestinal tumorigenesis through Src-mediated activation of the Wnt/β-catenin pathway [J]. Oncotarget, 2015, 6(5): 3123-35.
[254] ALKAN F K, KORKAYA H. Therapeutic utility of immunosuppressive TREM2+ macrophages: an important step forward in potentiating the immune checkpoint inhibitors [J]. Signal Transduction and Targeted Therapy, 2020, 5(1): 264.
[255] RAVETCH J V, LANIER L L. Immune inhibitory receptors [J]. Science, 2000, 290(5489): 84-9.
[256] RUMPRET M, DRYLEWICZ J, ACKERMANS L J E, et al. Functional categories of immune inhibitory receptors [J]. Nat Rev Immunol, 2020, 20(12): 771-80.
[257] DENG M, GUI X, KIM J, et al. LILRB4 signalling in leukaemia cells mediates T cell suppression and tumour infiltration [J]. Nature, 2018, 562(7728): 605-9.
[258] BARKAL A A, WEISKOPF K, KAO K S, et al. Engagement of MHC class I by the inhibitory receptor LILRB1 suppresses macrophages and is a target of cancer immunotherapy [J]. Nat Immunol, 2018, 19(1): 76-84.
[259] JIANG P, LAGENAUR C F, NARAYANAN V. Integrin-associated protein is a ligand for the P84 neural adhesion molecule [J]. J Biol Chem, 1999, 274(2): 559-62.
[260] BROWN E J, FRAZIER W A. Integrin-associated protein (CD47) and its ligands [J]. Trends Cell Biol, 2001, 11(3): 130-5.
[261] KHARITONENKOV A, CHEN Z, SURES I, et al. A family of proteins that inhibit signalling through tyrosine kinase receptors [J]. Nature, 1997, 386(6621): 181-6.
[262] FUJIOKA Y, MATOZAKI T, NOGUCHI T, et al. A novel membrane glycoprotein, SHPS-1, that binds the SH2-domain-containing protein tyrosine phosphatase SHP-2 in response to mitogens and cell adhesion [J]. Mol Cell Biol, 1996, 16(12): 6887-99.
[263] TIMMS J F, CARLBERG K, GU H, et al. Identification of major binding proteins and substrates for the SH2-containing protein tyrosine phosphatase SHP-1 in macrophages [J]. Mol Cell Biol, 1998, 18(7): 3838-50.
[264] ALVEY C M, SPINLER K R, IRIANTO J, et al. SIRPA-Inhibited, Marrow-Derived Macrophages Engorge, Accumulate, and Differentiate in Antibody-Targeted Regression of Solid Tumors [J]. Curr Biol, 2017, 27(14): 2065-77 e6.
[265] PAN L, WANG B, CHEN M, et al. Lack of SIRP-alpha reduces lung cancer growth in mice by promoting anti-tumour ability of macrophages and neutrophils [J]. Cell Prolif, 2023, 56(2): e13361.
[266] BIAN Z, SHI L, KIDDER K, et al. Intratumoral SIRPalpha-deficient macrophages activate tumor antigen-specific cytotoxic T cells under radiotherapy [J]. Nat Commun, 2021, 12(1): 3229.
[267] THOMMEN D S, SCHUMACHER T N. T Cell Dysfunction in Cancer [J]. Cancer Cell, 2018, 33(4): 547-62.
[268] ZHAO H, MING T, TANG S, et al. Wnt signaling in colorectal cancer: pathogenic role and therapeutic target [J]. Mol Cancer, 2022, 21(1): 144.
[269] YU R, ZHU B, CHEN D. Type I interferon-mediated tumor immunity and its role in immunotherapy [J]. Cell Mol Life Sci, 2022, 79(3): 191.
[270] HOUSE I G, SAVAS P, LAI J, et al. Macrophage-Derived CXCL9 and CXCL10 Are Required for Antitumor Immune Responses Following Immune Checkpoint Blockade [J]. Clin Cancer Res, 2020, 26(2): 487-504.
[271] OSTUNI R, KRATOCHVILL F, MURRAY P J, et al. Macrophages and cancer: from mechanisms to therapeutic implications [J]. Trends Immunol, 2015, 36(4): 229-39.
[272] CHEN D S, MELLMAN I. Oncology meets immunology: the cancer-immunity cycle [J]. Immunity, 2013, 39(1): 1-10.
[273] SANMAMED M F, CHEN L. A Paradigm Shift in Cancer Immunotherapy: From Enhancement to Normalization [J]. Cell, 2019, 176(3): 677.
[274] SHARPE A H. Introduction to checkpoint inhibitors and cancer immunotherapy [J]. Immunol Rev, 2017, 276(1): 5-8.
[275] DRAKE C G, LIPSON E J, BRAHMER J R. Breathing new life into immunotherapy: review of melanoma, lung and kidney cancer [J]. Nat Rev Clin Oncol, 2014, 11(1): 24-37.
[276] VESELY M D, ZHANG T, CHEN L. Resistance Mechanisms to Anti-PD Cancer Immunotherapy [J]. Annu Rev Immunol, 2022, 40: 45-74.
[277] KUBLI S P, BERGER T, ARAUJO D V, et al. Beyond immune checkpoint blockade: emerging immunological strategies [J]. Nat Rev Drug Discov, 2021, 20(12): 899-919.
[278] KEREN L, BOSSE M, MARQUEZ D, et al. A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging [J]. Cell, 2018, 174(6): 1373-87 e19.
[279] KIM I S, GAO Y, WELTE T, et al. Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms [J]. Nat Cell Biol, 2019, 21(9): 1113-26.
[280] GUBIN M M, ESAULOVA E, WARD J P, et al. High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy [J]. Cell, 2018, 175(4): 1014-30 e19.
[281] PITTET M J, MICHIELIN O, MIGLIORINI D. Clinical relevance of tumour-associated macrophages [J]. Nat Rev Clin Oncol, 2022, 19(6): 402-21.
[282] VEILLETTE A, CHEN J. SIRPalpha-CD47 Immune Checkpoint Blockade in Anticancer Therapy [J]. Trends Immunol, 2018, 39(3): 173-84.
[283] CHAO M P, WEISSMAN I L, MAJETI R. The CD47-SIRPalpha pathway in cancer immune evasion and potential therapeutic implications [J]. Curr Opin Immunol, 2012, 24(2): 225-32.
[284] FENG M, JIANG W, KIM B Y S, et al. Phagocytosis checkpoints as new targets for cancer immunotherapy [J]. Nat Rev Cancer, 2019, 19(10): 568-86.
[285] JAISWAL S, JAMIESON C H, PANG W W, et al. CD47 is upregulated on circulating hematopoietic stem cells and leukemia cells to avoid phagocytosis [J]. Cell, 2009, 138(2): 271-85.
[286] MAJETI R, CHAO M P, ALIZADEH A A, et al. CD47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells [J]. Cell, 2009, 138(2): 286-99.
[287] CHAO M P, ALIZADEH A A, TANG C, et al. Anti-CD47 antibody synergizes with rituximab to promote phagocytosis and eradicate non-Hodgkin lymphoma [J]. Cell, 2010, 142(5): 699-713.
[288] HUTTER G, THERUVATH J, GRAEF C M, et al. Microglia are effector cells of CD47-SIRPalpha antiphagocytic axis disruption against glioblastoma [J]. Proc Natl Acad Sci U S A, 2019, 116(3): 997-1006.
[289] INGRAM J R, BLOMBERG O S, SOCKOLOSKY J T, et al. Localized CD47 blockade enhances immunotherapy for murine melanoma [J]. Proc Natl Acad Sci U S A, 2017, 114(38): 10184-9.
[290] CHEN J, ZHONG M C, GUO H, et al. SLAMF7 is critical for phagocytosis of haematopoietic tumour cells via Mac-1 integrin [J]. Nature, 2017, 544(7651): 493-7.
[291] ADVANI R, FLINN I, POPPLEWELL L, et al. CD47 Blockade by Hu5F9-G4 and Rituximab in Non-Hodgkin's Lymphoma [J]. N Engl J Med, 2018, 379(18): 1711-21.
[292] VEGLIA F, PEREGO M, GABRILOVICH D. Myeloid-derived suppressor cells coming of age [J]. Nat Immunol, 2018, 19(2): 108-19.
[293] HUANG C, WANG X, WANG Y, et al. Sirpα on tumor-associated myeloid cells restrains antitumor immunity in colorectal cancer independent of its interaction with CD47 [J]. Nature Cancer, 2024, 5(3): 500-16.
[294] AHN E, ARAKI K, HASHIMOTO M, et al. Role of PD-1 during effector CD8 T cell differentiation [J]. Proc Natl Acad Sci U S A, 2018, 115(18): 4749-54.
[295] MENSALI N, GRENOV A, PATI N B, et al. Antigen-delivery through invariant chain (CD74) boosts CD8 and CD4 T cell immunity [J]. Oncoimmunology, 2019, 8(3): 1558663.
[296] OKAZAWA H, MOTEGI S, OHYAMA N, et al. Negative regulation of phagocytosis in macrophages by the CD47-SHPS-1 system [J]. J Immunol, 2005, 174(4): 2004-11.
[297] DONG H, STROME S E, SALOMAO D R, et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion [J]. Nat Med, 2002, 8(8): 793-800.
[298] WALDMAN A D, FRITZ J M, LENARDO M J. A guide to cancer immunotherapy: from T cell basic science to clinical practice [J]. Nat Rev Immunol, 2020, 20(11): 651-68.
[299] KHALIL D N, SMITH E L, BRENTJENS R J, et al. The future of cancer treatment: immunomodulation, CARs and combination immunotherapy [J]. Nat Rev Clin Oncol, 2016, 13(5): 273-90.
[300] REN X, ZHANG L, ZHANG Y, et al. Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment [J]. Annu Rev Immunol, 2021, 39: 583-609.
[301] MOLGORA M, ESAULOVA E, VERMI W, et al. TREM2 Modulation Remodels the Tumor Myeloid Landscape Enhancing Anti-PD-1 Immunotherapy [J]. Cell, 2020, 182(4): 886-900 e17.
[302] ZHAO X W, VAN BEEK E M, SCHORNAGEL K, et al. CD47-signal regulatory protein-alpha (SIRPalpha) interactions form a barrier for antibody-mediated tumor cell destruction [J]. Proc Natl Acad Sci U S A, 2011, 108(45): 18342-7.
[303] RING N G, HERNDLER-BRANDSTETTER D, WEISKOPF K, et al. Anti-SIRPalpha antibody immunotherapy enhances neutrophil and macrophage antitumor activity [J]. Proc Natl Acad Sci U S A, 2017, 114(49): E10578-E85.
[304] VAN HELDEN M J, ZWARTHOFF S A, ARENDS R J, et al. BYON4228 is a pan-allelic antagonistic SIRPalpha antibody that potentiates destruction of antibody-opsonized tumor cells and lacks binding to SIRPgamma on T cells [J]. J Immunother Cancer, 2023, 11(4).
[305] GAUTTIER V, PENGAM S, DURAND J, et al. Selective SIRPalpha blockade reverses tumor T cell exclusion and overcomes cancer immunotherapy resistance [J]. J Clin Invest, 2020.
[306] WILLINGHAM S B, VOLKMER J P, GENTLES A J, et al. The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors [J]. Proc Natl Acad Sci U S A, 2012, 109(17): 6662-7.
[307] LIU X, PU Y, CRON K, et al. CD47 blockade triggers T cell-mediated destruction of immunogenic tumors [J]. Nat Med, 2015, 21(10): 1209-15.
[308] D. TSENG, J.-P. VOLKMER, B. WILLINGHAM S, et al. Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response [J]. Proceedings of the National Academy of Sciences, 2013, 110(27): 11103-8.
[309] THERUVATH J, MENARD M, SMITH B A H, et al. Anti-GD2 synergizes with CD47 blockade to mediate tumor eradication [J]. Nat Med, 2022, 28(2): 333-44.
[310] LIU M, O'CONNOR R S, TREFELY S, et al. Metabolic rewiring of macrophages by CpG potentiates clearance of cancer cells and overcomes tumor-expressed CD47-mediated 'don't-eat-me' signal [J]. Nat Immunol, 2019, 20(3): 265-75.
[311] SIM J, SOCKOLOSKY J T, SANGALANG E, et al. Discovery of high affinity, pan-allelic, and pan-mammalian reactive antibodies against the myeloid checkpoint receptor SIRPalpha [J]. MAbs, 2019, 11(6): 1036-52.
[312] JHUNJHUNWALA S, HAMMER C, DELAMARRE L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion [J]. Nat Rev Cancer, 2021, 21(5): 298-312.
[313] KROEMER G, GALASSI C, ZITVOGEL L, et al. Immunogenic cell stress and death [J]. Nat Immunol, 2022, 23(4): 487-500.
[314] RIGHELLI D, SOTTOSANTI A, RISSO D. Designing spatial transcriptomic experiments [J]. Nature Methods, 2023, 20(3): 355-6.
[315] YANG R, XU T, ZHANG L, et al. A single-cell atlas depicting the cellular and molecular features in human anterior cruciate ligamental degeneration: A single cell combined spatial transcriptomics study [J]. Elife, 2023, 12.
[316] WEI H, WANG J Y. Role of Polymeric Immunoglobulin Receptor in IgA and IgM Transcytosis [J]. Int J Mol Sci, 2021, 22(5).
[317] BRANDTZAEG P, PRYDZ H. Direct evidence for an integrated function of J chain and secretory component in epithelial transport of immunoglobulins [J]. Nature, 1984, 311(5981): 71-3.
[318] SEIKRIT C, PABST O. The immune landscape of IgA induction in the gut [J]. Semin Immunopathol, 2021, 43(5): 627-37.
[319] WANG S, CHEN Y G. BMP signaling in homeostasis, transformation and inflammatory response of intestinal epithelium [J]. Sci China Life Sci, 2018, 61(7): 800-7.
[320] BINNERTS M E, KIM K A, BRIGHT J M, et al. R-Spondin1 regulates Wnt signaling by inhibiting internalization of LRP6 [J]. Proc Natl Acad Sci U S A, 2007, 104(37): 14700-5.
[321] LEBENSOHN A M, ROHATGI R. R-spondins can potentiate WNT signaling without LGRs [J]. Elife, 2018, 7.
[322] SCHULTE G, BRYJA V. The Frizzled family of unconventional G-protein-coupled receptors [J]. Trends Pharmacol Sci, 2007, 28(10): 518-25.
[323] FRE S, HUYGHE M, MOURIKIS P, et al. Notch signals control the fate of immature progenitor cells in the intestine [J]. Nature, 2005, 435(7044): 964-8.
[324] STANGER B Z, DATAR R, MURTAUGH L C, et al. Direct regulation of intestinal fate by Notch [J]. Proc Natl Acad Sci U S A, 2005, 102(35): 12443-8.
[325] ITO K, LIM A C, SALTO-TELLEZ M, et al. RUNX3 attenuates beta-catenin/T cell factors in intestinal tumorigenesis [J]. Cancer Cell, 2008, 14(3): 226-37.
[326] PRABHU K S. The selenoprotein P-LRP5/6-WNT3A complex promotes tumorigenesis in sporadic colorectal cancer [J]. J Clin Invest, 2023, 133(13).
[327] WANG G, BONKOVSKY H L, DE LEMOS A, et al. Recent insights into the biological functions of liver fatty acid binding protein 1 [J]. J Lipid Res, 2015, 56(12): 2238-47.
[328] MCKILLOP I H, GIRARDI C A, THOMPSON K J. Role of fatty acid binding proteins (FABPs) in cancer development and progression [J]. Cellular Signalling, 2019, 62: 109336.
[329] LIU L Z, ZHANG Z, ZHENG B H, et al. CCL15 Recruits Suppressive Monocytes to Facilitate Immune Escape and Disease Progression in Hepatocellular Carcinoma [J]. Hepatology, 2019, 69(1): 143-59.
[330] JI L, QIAN W, GUI L, et al. Blockade of β-Catenin-Induced CCL28 Suppresses Gastric Cancer Progression via Inhibition of Treg Cell Infiltration [J]. Cancer Res, 2020, 80(10): 2004-16.
[331] WANG S, WANG J, CHEN Z, et al. Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance [J]. npj Precision Oncology, 2024, 8(1): 31.
[332] ZHANG R, QI F, ZHAO F, et al. Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer [J]. Cell Death Dis, 2019, 10(4): 273.
[333] ROJAS R, APODACA G. Immunoglobulin transport across polarized epithelial cells [J]. Nature Reviews Molecular Cell Biology, 2002, 3(12): 944-56.
[334] TOKUNAGA R, ZHANG W, NASEEM M, et al. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy [J]. Cancer Treat Rev, 2018, 63: 40-7.
[335] COLONNA M. The biology of TREM receptors [J]. Nature Reviews Immunology, 2023, 23(9): 580-94.
[336] BINNEWIES M, POLLACK J L, RUDOLPH J, et al. Targeting TREM2 on tumor-associated macrophages enhances immunotherapy [J]. Cell Rep, 2021, 37(3): 109844.
[337] HAND T W, REBOLDI A. Production and Function of Immunoglobulin A [J]. Annu Rev Immunol, 2021, 39: 695-718.
[338] GOMMERMAN J L, ROJAS O L, FRITZ J H. Re-thinking the functions of IgA(+) plasma cells [J]. Gut Microbes, 2014, 5(5): 652-62.
[339] MEYLAN M, PETITPREZ F, BECHT E, et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer [J]. Immunity, 2022, 55(3): 527-41.e5.
[340] KDIMATI S, MULLINS C S, LINNEBACHER M. Cancer-Cell-Derived IgG and Its Potential Role in Tumor Development [J]. Int J Mol Sci, 2021, 22(21).
[341] WANG J, LIN D, PENG H, et al. Cancer-derived immunoglobulin G promotes tumor cell growth and proliferation through inducing production of reactive oxygen species [J]. Cell Death Dis, 2013, 4(12): e945.