Recent studies have demonstrated dramatic shifts in metabolic supply-and-demand ratios during inflammation, a process resulting in localized tissue hypoxia within inflammatory lesions (“inflammatory hypoxia”). As part of the adaptive immune response, T cells are recruited to sites of inflammatory hypoxia. Given the profound effects of hypoxia on gene regulation, we hypothesized that T-cell differentiation is controlled by hypoxia. To pursue this hypothesis, we analyzed the transcriptional consequences of ambient hypoxia (1% oxygen) on a broad panel of T-cell differentiation factors. Surprisingly, these studies revealed selective, robust induction of FoxP3, a key transcriptional regulator for regulatory T cells (Tregs). Studies of promoter binding or loss- and gain-of-function implicated hypoxia-inducible factor (HIF)-1α in inducing FoxP3. Similarly, hypoxia enhanced Treg abundance in vitro and in vivo. Finally, Treg-intrinsic HIF-1α was required for optimal Treg function and Hif1a –deficient Tregs failed to control T-cell–mediated colitis. These studies demonstrate that hypoxia is an intrinsic molecular cue that promotes FoxP3 expression, in turn eliciting potent anti-inflammatory mechanisms to limit tissue damage in conditions of reduced oxygen availability.

Intercellular transfer of miR-223 from neutrophils to alveolar epithelial cells reduces lung inflammation in a mouse model of ventilator-induced lung injury or pulmonary bacterial infection.

Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. For the beginner, however, the large number of algorithms that have been developed, as well as the lack of consensus on best practices for analyzing these data, raise multiple questions: Which algorithm is the best for analyzing a dataset? How do different algorithms compare? How can one move beyond data visualization to gain new biological insights? In this article, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five cytometry by time-of-flight analysis platforms (viSNE, SPADE, X-shift, PhenoGraph, and Citrus), we identify important considerations and challenges that users should be aware of when using these different methods and common and unique insights that can be revealed by these different methods. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. In total, these analyses emphasize the benefits of integrating multiple cytometry by time-of-flight analysis algorithms to gain complementary insights into these high-dimensional datasets.

MHC class II (MHCII) expression is usually restricted to APC but can be expressed by cancer cells. We examined the effect of cancer cell–specific MHCII (csMHCII) expression in lung adenocarcinoma on T cell recruitment to tumors and response to anti–PD-1 therapy using two orthotopic immunocompetent murine models of non–small cell lung cancer: CMT167 (CMT) and Lewis lung carcinoma (LLC). We previously showed that CMT167 tumors are eradicated by anti-PD1 therapy, whereas LLC tumors are resistant. RNA sequencing analysis of cancer cells recovered from tumors revealed that csMHCII correlated with response to anti-PD1 therapy, with immunotherapy-sensitive CMT167 cells being csMHCII positive, whereas resistant LLC cells were csMHCII negative. To test the functional effects of csMHCII, MHCII expression was altered on the cancer cells through loss- and gain-of-function of CIITA, a master regulator of the MHCII pathway. Loss of CIITA in CMT167 decreased csMHCII and converted tumors from anti–PD-1 sensitive to anti–PD-1 resistant. This was associated with lower levels of Th1 cytokines, decreased T cell infiltration, increased B cell numbers, and decreased macrophage recruitment. Conversely, overexpression of CIITA in LLC cells resulted in csMHCII in vitro and in vivo. Enforced expression of CIITA increased T cell infiltration and sensitized tumors to anti–PD-1 therapy. csMHCII expression was also examined in a subset of surgically resected human lung adenocarcinomas by multispectral imaging, which provided a survival benefit and positively correlated with T cell infiltration. These studies demonstrate a functional role for csMHCII in regulating T cell infiltration and sensitivity to anti–PD-1.

While a fraction of cancer patients treated with anti–PD-1 show durable therapeutic responses, most remain unresponsive, highlighting the need to better understand and improve these therapies. Using an in vivo screening approach with a customized shRNA pooled library, we identified DDR2 as a leading target for the enhancement of response to anti–PD-1 immunotherapy. Using isogenic in vivo murine models across five different tumor histologies—bladder, breast, colon, sarcoma, and melanoma—we show that DDR2 depletion increases sensitivity to anti–PD-1 treatment compared to monotherapy. Combination treatment of tumor-bearing mice with anti–PD-1 and dasatinib, a tyrosine kinase inhibitor of DDR2, led to tumor load reduction. RNA-seq and CyTOF analysis revealed higher CD8⁺ T cell populations in tumors with DDR2 depletion and those treated with dasatinib when either was combined with anti–PD-1 treatment. Our work provides strong scientific rationale for targeting DDR2 in combination with PD-1 inhibitors.