Fibroblasts play critical roles in regulating cellular relationships during tissue homeostasis, immunity, and tumor biology at multiple sites. However, tools to perturb fibroblasts at just one site in vivo are limited, restricting our understanding of how these cellular relationships act locally. We optimized local gene editing of fibroblasts in mouse tumor models to investigate how fibroblast perturbations affect the tumor microenvironment (TME). By knocking out receptors Osmr, Tgfbr2, or Il1r1 on cancer-associated fibroblasts (CAFs), we uncover that TGFBR2 signaling loss induces the emergence of a new Col18a1hi CAF cell state that is associated with worse survival in pancreatic cancer patients. Combinatorial gene KOs in CAFs reveals a circuit where these Col18a1hi CAFs reshape the TME by recruiting Siglec-Fhi neutrophils via Cxcl5 expression, and where this Col18a1hi CAF cell state is dependent on TNFR1 and canonical Wnt signaling. Together, a fast, affordable, and modular engineering method is demonstrated, allowing discovery of modified fibroblast identities and local intercellular relationships in the TME.

Mouse models are frequently used to develop treatments for human cancer. However, the degree to which their tumor microenvironments (TMEs) are synonymously assembled is particularly poorly characterized. Through systematic immunoprofiling of 15 commonly used mouse models, we found that most murine TMEs recapitulate the composition of poorly infiltrated human tumors, extensively biased toward high macrophage densities. We discovered substantial species-specific biases of chemokine expression networks known to drive TMEs assembly, together with discoordinated frequencies of T and myeloid cell subtypes. Even with variable alignment, conserved cell-type-specific gene expression programs emerged across species and cohorts. Dissecting the coordinated T cell-myeloid gene expression programs revealed a conserved axis between interferon-responsive myeloid states and ongoing T cell cytotoxicity that transcends tissue of origin and predicts clinical outcome. Collectively, this work provides a practical atlas outlining both the hazards and opportunities of using mice to model human cancer.

An effective immune system must sample and develop healthy self-identity to prevent autoimmunity and to discern pathogenic insults1-3. Self-proteins are presented to T cells in the thymus during immune cell development2,3 and must be presented throughout the body to maintain regulatory T cell populations4-6 and to provide tonic signals to sustain conventional T cells over time7-9. Observations of continuous apoptosis in some organs together with the ingestion of that material by myeloid populations has led to a conventional understanding of ongoing cell death as a major source of self-antigens10. Here we used a series of companion imaging and vesicular labelling technologies to reveal an alternative process undertaken by macrophages that results in non-destructive, direct sampling of living cells. This process requires cell-cell contact, does not require caspase activation and occurs via trogocytosis-like stretching of the target cell into the macrophage, which leads to the generation of submicrometre-sized vesicles that contain cytoplasm. Using a high-dimensional flow-based method for labelling vesicles, we demonstrate that live-sampled material is distinctly processed and is poorly subjected to fusion with lysosomes. The material also produces differential effects on the presentation of antigen to CD4 T cells compared with CD8 T cells. Disruption of this trafficking by redirecting antigen to the lysosome significantly reduced the associated macrophage-mediated priming of CD8 T cells. These results demonstrate an important and substantial sampling of living cells by the immune system, with clear consequences for maintaining the border of immunity.

Undescribed functional axes may intersect with the trajectory of T cell exhaustion (TEX) to contribute to the antitumoral functions of CD8 T cells. By leveraging fluorescent transcriptional reporting of the T cell activation marker Cd69, we defined a classifier for potent versus suboptimal CD69+ activation states arising from T cell stimulation. In tumors, this delineation provided an additional functional readout among TEX subsets, marked by enhanced effector molecule production. The more potent Cd69-TFPhi state was the most prominent in a T cell-mediated tumor clearance model, displaying increased engagement and superior tumor cell killing. Simultaneous analysis of gene and protein expression in human head and neck tumors enabled a similar strategy to identify Cd69RNAhiCD69+ cells with enhanced functional features compared with Cd69RNAloCD69+ cells among intratumoral CD8 T cell subsets. Thus, refining the T cell functional landscape in tumors potentiates the identification of rare, potent effectors that could be leveraged for improving cancer treatment.

Fibroblasts play critical roles in regulating cellular relationships during tissue homeostasis, immunity, and tumor biology at multiple sites. However, tools to perturb fibroblasts at just one site in vivo are limited, restricting our understanding of how these cellular relationships develop on a local level. We optimized local gene editing of fibroblasts in multiple mouse tumor models to investigate how locally restricted fibroblast perturbations affect the cellular tumor microenvironment (TME). By knocking out surface receptors Osmr, Tgfbr2, or Il1r1 on cancer-associated fibroblasts (CAFs), we uncover that TGFBR2 signaling loss uniquely induces the emergence of a Col18a1 hi CAF cell state that is distinct from previously described fibroblast states and is associated with worse survival in human PDAC patients. Further application of a local as well as combinatorial gene knockout technology in CAFs reveals a circuit in which these Col18a1 hi CAFs reshape the TME by recruiting Siglec-Fhi neutrophils via Cxcl5 expression; and that the Col18a1 hi CAF cell state is further dependent on TNFR1 and canonical Wnt signaling. Together, a fast, affordable, and modular engineering method is demonstrated, allowing discovery of a modified fibroblast identify, as well as the network details of a local inter-cellular circuitry in a tumor.

Intravital microscopy has enabled the study of immune dynamics in the pulmonary microvasculature, but many key events remain unseen because they occur in deeper lung regions. We therefore developed a technique for stabilized intravital imaging of bronchovascular cuffs and collecting lymphatics surrounding pulmonary veins in mice. Intravital imaging of pulmonary lymphatics revealed ventilation-dependence of steady-state lung lymph flow and ventilation-independent lymph flow during inflammation. We imaged the rapid exodus of migratory dendritic cells through lung lymphatics following inflammation and measured effects of pharmacologic and genetic interventions targeting chemokine signaling. Intravital imaging also captured lymphatic immune surveillance of lung-metastatic cancers and lymphatic metastasis of cancer cells. To our knowledge, this is the first imaging of lymph flow and leukocyte migration through intact pulmonary lymphatics. This approach will enable studies of protective and maladaptive processes unfolding within the lungs and in other previously inaccessible locations.

Intratumoral heterogeneity (ITH)-defined as genetic and cellular diversity within a tumor-is linked to failure of immunotherapy and an inferior anti-tumor immune response. We modeled heterogeneous tumors comprised of "hot" and "cold" tumor populations (giving rise to T cell-rich and T cell-poor tumors, respectively) and introduced fluorescent labels to enable precise spatial tracking. We found the cold tumor cell population exerted a "dominant cold" effect in mixed tumors. Strikingly, spatial analysis revealed that the tumor cells themselves created distinct local microenvironments within heterogeneous tumors: regions occupied by cold tumor cells showed pronounced immunosuppression, harboring increased CD206Hi macrophages and diminished local T cell function. This inferior T cell activity in cold regions persisted even after immunotherapy and mechanistically was mediated by CX3CL1 produced by the cold tumor cells. An immune cold tumor population within a heterogeneous tumor thus impairs tumor immunity on both a tumor-wide and a highly localized spatial scale.

Mouse models are frequently used to develop treatments for human cancer. Yet, we lack a comprehensive understanding of the comparative organization of mouse and human tumor microenvironments (mu/huTMEs). Through immunoprofiling of commonly used mouse models, we found that the immune composition of most muTMEs resemble poorly infiltrated human tumors extensively biased toward high macrophages densities. Relatedly, we discover species-specific biases of chemokine expression networks, factors which drive TMEs assembly. Further, assessing coarse cellular networks, we find conserved correlations between some immune cell frequencies, while other relationships only appear conserved in the huTMEs matching muTME profiles. Despite this variable alignment, we define robust cell type-specific gene expression programs conserved in TMEs across species and cohorts and identify ones that are coordinated between cell populations in both species. Together, we isolate and offer methods to study the multiple areas of hazard and opportunities for using mice to model human cancer. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Delirium is a neurologic syndrome characterized by inattention and cognitive impairment frequently encountered in the medically ill. Peripheral inflammation is a key trigger of delirium, but the patient-specific immune responses associated with delirium development and resolution are unknown. This retrospective cohort study of prospectively collected biospecimens examines RNA sequencing from peripheral blood mononuclear cells of adults hospitalized for COVID-19 to better understand patient-specific factors associated with delirium (n = 64). Longitudinal transcriptomic analyses highlight persistent immune dysregulation in delirium, marked by increasing expression trajectories of genes linked to innate immune pathways, including complement activation, cytokine production, and monocyte/macrophage recruitment. Genes involved adaptive immunity showed a declining trajectory over time in patients with delirium. Although corticosteroid treatment suppressed some aspects of immune hyperactivation, aberrant responses contributing to delirium were exacerbated. Delirium resolution was characterized by normalization of key transcripts such as CCL2 and innate immune markers. Novel associations with delirium were found in genes related to stress granule assembly and DUSP2 and KLF10 , which mediate T-cell responses. These findings provide insights into the peripheral immune responses accompanying delirium and their modulation by corticosteroids. Future trials targeting aberrant inflammatory responses may mitigate the severe outcomes associated with delirium due to COVID19.

BACKGROUNDAccurate prognostic assays for COVID-19 represent an unmet clinical need. We sought to identify and validate early parsimonious transcriptomic signatures that accurately predict fatal outcomes.METHODSWe studied 894 patients enrolled in the prospective, multicenter Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) with peripheral blood mononuclear cells (PBMC) and nasal swabs collected within 48 hours of admission. Host gene expression was measured with RNA-Seq. We trained parsimonious prognostic classifiers incorporating host gene expression, age, and SARS-CoV-2 viral load to predict 28-day mortality in 70% of the cohort. Classifier performance was determined in the remaining 30% and externally validated in a contemporary COVID-19 cohort (n = 137) with vaccinated patients.RESULTSFatal COVID-19 was characterized by 4,189 differentially expressed genes in the peripheral blood. A COVID-specific 3-gene peripheral blood classifier (CD83, ATP1B2, DAAM2) combined with age and SARS-CoV-2 viral load achieved an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.82-0.94). A 3-gene nasal classifier (SLC5A5, CD200R1, FCER1A), in comparison, yielded an AUC of 0.74 (95% CI, 0.64-0.83). Notably, OLAH, the most strongly upregulated gene in both PBMC and nasal swab and recently implicated in severe viral infection pathogenesis, yielded AUCs of 0.86 (0.79-0.93) and 0.78 (95% CI, 0.69-0.86), respectively. Both peripheral blood classifiers demonstrated comparable performance in an independent contemporary cohort of vaccinated patients (AUCs 0.74-0.80).CONCLUSIONOur parsimonious blood- and nasal-based classifiers accurately predicted COVID-19 mortality and merit further study as accessible prognostic tools to guide triage, resource allocation, and early therapeutic interventions.FUNDINGNIH: 5R01AI135803-03, R35HL140026, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, 5T32DA018926-18, and K0826161611. National Institute of Allergy and Infectious Diseases, NIH: 3U19AI1289130, U19AI128913-04S1, and R01AI122220. National Center for Advancing Translational Sciences, NIH: UM1TR004528. The National Science Foundation: DMS2310836. The Chan Zuckerberg Biohub San Francisco.

Peer review serves as the cornerstone of scientific quality control. Yet, the current journal-centric system is hindered by long timelines, high publication costs, inconsistent review quality, systemic biases, and editorial gatekeeping. Notably, the system is built around misaligned measures of impact that are tethered to journal branding and conflate scientific rigor ( Quality ) with perceived significance ( Impact ). Here, we report findings from the Discovery Stack Pilot Study, which tested a scientist-designed, journal-independent peer review model. The Discovery Stack model integrates in-line reviewer comments to promote constructive, improvement-focused feedback and generates separate, multimodal assessments of scientific Quality and Impact . To examine its feasibility and effectiveness, manuscripts enrolled in the pilot were reviewed in parallel with traditional journal review. A total of 162 reviews were completed, and survey data from 86 participants were analyzed to evaluate the experience of both authors and reviewers. The results showed that reviewers effectively evaluated Quality and Impact as separate dimensions, with Quality scores being more consistent across reviewers than Impact scores. Importantly, participants strongly supported the core elements of the Discovery Stack model and expressed enthusiasm for its broader adoption to enhance transparency, efficiency, and value in peer review. Future studies will explore integrating this model into a digital platform for reviewing and curating scientific discoveries to improve the production and dissemination of high-quality research. ### Competing Interest Statement The authors have declared no competing interest. Solving For Science

The anti-tumor function of CD8 T cells is limited through well-established pathways of T cell exhaustion (TEX). Strategies to capture emergent functional states amongst this dominant trajectory of dysfunction are necessary to find pathways to durable anti-tumor immunity. By leveraging transcriptional reporting (by the fluorescent protein TFP) of the T cell activation marker Cd69, related to upstream AP-1 transcription factors, we define a classifier for potent versus suboptimal CD69+ activation states arising from T cell stimulation. In tumors, this delineation acts an additional functional readout along the TEX differentiation trajectory, within and across TEX subsets, marked by enhanced effector cytokine and granzyme B production. The more potent state remains differentially prominent in a T cell-mediated tumor clearance model, where they also show increased engagement in the microenvironment and are superior in tumor cell killing. Employing multimodal CITE-Seq in human head and neck tumors enables a similar strategy to identify Cd69RNAhiCD69+ cells that also have enhanced functional features in comparison to Cd69RNAloCD69+ cells, again within and across intratumoral CD8 T cell subsets. Refining the contours of the T cell functional landscape in tumors in this way paves the way for the identification of rare exceptional effectors, with imminent relevance to cancer treatment.

Rationale: Two molecular phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and responses to therapy have been identified. Classification as "hyperinflammatory" or "hypoinflammatory" depends on plasma biomarker profiling. Limited data are available about the differences in the pulmonary biology of the molecular phenotypes. Objectives: To identify differences in the pulmonary biology of ARDS molecular phenotypes Methods: We compared tracheal aspirate gene expression between hyperinflammatory and hypoinflammatory phenotypes in bulk RNA sequencing (RNASeq) from coronavirus disease (COVID-19) and non-COVID-19 ARDS and single-cell RNASeq from non-COVID-19 ARDS. In a subset of subjects, we also compared plasma proteomic data. Measurements and Main Results: In bulk RNASeq analyses, 1,157 genes were differentially expressed (false discovery rate < 0.1) between phenotypes in non-COVID-19 ARDS, and 85 genes were differentially expressed between phenotypes in COVID-19 ARDS. Eighteen genes were reproducibly differentially expressed between phenotypes in both cohorts, including greater expression of IL32, HSPA8, and PPP3CC in hyperinflammatory ARDS. A total of 195 pathways were reproducibly enriched across the two cohorts by gene set enrichment analysis, including greater expression of granulopoiesis, T-cell and IFN signaling, and integrated stress response pathways in hyperinflammatory ARDS. Network analysis of single-cell RNASeq in a third group of patients identified greater T-cell signaling to other immune cells in hyperinflammatory ARDS. Conclusions: Hyperinflammatory and hypoinflammatory ARDS molecular phenotypes have distinct respiratory biology. Hyperinflammatory ARDS is characterized by an increased IFN-stimulated gene expression and T-cell activation in the lungs.

Elimination of tumors is typically dependent on T cells, which require prior or ongoing activation signals. These dependencies form the basis for our understanding of "tumor-reactive immunity" and for the successes of immunotherapies, particularly immune checkpoint blockades. Over the years, tremendous work has been done towards understanding the biology of this reactive immunity and early investigation identified dendritic cells (DC) as key contributors. Recent advances have shed more light on dendritic cell heterogeneity in tumors, revealing specialized roles for each subtype. In addition, the network of cellular interactions surrounding DCs has grown as additional cell types have been revealed to variously influence how the immune system can become most effective at eliminating malignancies. Greater understanding of intratumoral DC biology has empowered investigators to engineer dendritic cell vaccines and consider other approaches to augment this component of reactive immunity, towards the generation of anti-tumor immune responses de novo. In this review, we will discuss the state of the field, recent advances and suggest what the near future of scientific inquiry could entail.

Supplementary Figures, Table, Supplementary Tables

Predicting mortality risk in patients with COVID-19 remains challenging, and accurate prognostic assays represent a persistent unmet clinical need. We aimed to identify and validate parsimonious transcriptomic signatures that accurately predict fatal outcomes within 48 hours of hospitalization. We studied 894 patients hospitalized for COVID-19 across 20 US hospitals and enrolled in the prospective Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) with peripheral blood mononuclear cells (PBMC) and nasal swabs collected within 48 hours of admission. Host gene expression was assessed by RNA sequencing, nasal SARS-CoV-2 viral load was measured by RT-qPCR, and mortality was assessed at 28 days. We first defined transcriptional signatures and biological features of fatal COVID-19, which we compared against mortality signatures from an independent cohort of patients with non-COVID-19 sepsis (n=122). Using least absolute shrinkage and selection operator (LASSO) regression in 70% of the COVID-19 cohort, we trained parsimonious prognostic classifiers incorporating host gene expression, age, and viral load. The performance of single and three-gene classifiers was then determined in the remaining 30% of the cohort and subsequently externally validated in an independent, contemporary COVID-19 cohort (n=137) with vaccinated patients. Fatal COVID-19 was characterized by 4189 differentially expressed genes in the peripheral blood, representing marked upregulation of neutrophil degranulation, erythrocyte gas exchange, and heme biosynthesis pathways, juxtaposed against downregulation of adaptive immune pathways. Only 7.6% of mortality-associated genes overlapped between COVID-19 and sepsis due to other causes. A COVID-specific three-gene peripheral blood classifier ( CD83, ATP1B2, DAAM2 ) combined with age and SARS-CoV-2 viral load achieved an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.82–0.94). A three-gene nasal classifier ( SLC5A5, CD200R1 , FCER1A ), in comparison, yielded an AUC of 0.74 (95% CI 0.64-0.83). Notably the expression of OLAH alone, a gene recently implicated in severe viral infection pathogenesis, yielded an AUC of 0.86 (0.79–0.93). Both peripheral blood classifiers demonstrated comparable performance in vaccinated patients from an independent external validation cohort (AUCs 0.74– 0.80). A three-gene peripheral blood signature, as well as OLAH alone, accurately predict COVID-19 mortality early in hospitalization, including in vaccinated patients. These parsimonious blood- and nasal-based classifiers merit further study as accessible prognostic tools to guide triage, resource allocation, and early therapeutic interventions in COVID-19.

Delirium is a neurologic syndrome characterized by inattention and cognitive impairment frequently encountered in medically ill older adults. As a hallmark of age-related brain vulnerability, delirium offers a clinical model to investigate how peripheral immune responses contribute to acute brain dysfunction. Peripheral inflammation is a key trigger of delirium, but the patient-specific immune responses that drive delirium onset and recovery remain poorly understood. This retrospective cohort study of prospectively collected biospecimens examines RNA sequencing from peripheral blood mononuclear cells of adults hospitalized for COVID-19 to better understand patient-specific factors associated with delirium (n = 64). Longitudinal transcriptomic analyses highlight persistent immune dysregulation in delirium, marked by increasing expression trajectories of genes linked to innate immune pathways, including complement activation, cytokine production, and monocyte/macrophage recruitment. Genes involved adaptive immunity showed a declining trajectory over time in patients with delirium. Although corticosteroid treatment suppressed some aspects of immune hyperactivation, aberrant responses contributing to delirium were exacerbated. Delirium resolution was characterized by normalization of key transcripts such as CCL2 and innate immune markers. Novel associations with delirium included transcripts related to stress granule assembly and the T cell regulators DUSP2 and KLF10. Delirium in COVID-19 is associated with distinct and dynamic peripheral immune trajectories that are modulated by corticosteroids. Further understanding these mechanisms has important implications for preventing delirium in older adults. These findings provide novel mechanistic insights with translational relevance for immunomodulatory strategies targeting maladaptive immune responses to prevent or treat delirium in medically ill populations.

We propose an emerging strategy for advanced cancer treatment based on progressive, stepwise remodeling of tumor microenvironments (TMEs). TMEs are variable but show conserved archetypes across patients and tissue origins. Deep learning over single-cell atlases collected from perturbed tumors can uncover gene and cellular networks shifting between archetypes. This allows for designing "nudge" or "state-shifting" drugs whose sequential application achieves stepwise transformation of a TME from an adverse to a more favorable state, dismantling deleterious tumor-host interactions to achieve patient remission.

In the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, epithelial populations in the distal lung expressing Angiotensin-converting enzyme 2 (ACE2) are infrequent, and therefore, the model of viral expansion and immune cell engagement remains incompletely understood. Using human lungs to investigate early host-viral pathogenesis, we found that SARS-CoV-2 had a rapid and specific tropism for myeloid populations. Human alveolar macrophages (AMs) reliably expressed ACE2 allowing both spike-ACE2–dependent viral entry and infection. In contrast to Influenza A virus, SARS-CoV-2 infection of AMs was productive, amplifying viral titers. While AMs generated new viruses, the interferon responses to SARS-CoV-2 were muted, hiding the viral dissemination from specific antiviral immune responses. The reliable and veiled viral depot in myeloid cells in the very early phases of SARS-CoV-2 infection of human lungs enables viral expansion in the distal lung and potentially licenses subsequent immune pathologies.