Baricitinib treatment resolves lower-airway macrophage inflammation and neutrophil recruitment in SARS-CoV-2-infected rhesus macaques.

SARS-CoV-2-induced hypercytokinemia and inflammation are critically associated with COVID-19 severity. Baricitinib, a clinically approved JAK1/JAK2 inhibitor, is currently being investigated in COVID-19 clinical trials. Here, we investigated the immunologic and virologic efficacy of baricitinib in a rhesus macaque model of SARS-CoV-2 infection. Viral shedding measured from nasal and throat swabs, bronchoalveolar lavages, and tissues was not reduced with baricitinib. Type I interferon (IFN) antiviral responses and SARS-CoV-2-specific T cell responses remained similar between the two groups. Animals treated with baricitinib showed reduced inflammation, decreased lung infiltration of inflammatory cells, reduced NETosis activity, and more limited lung pathology. Importantly, baricitinib-treated animals had a rapid and remarkably potent suppression of lung macrophage production of cytokines and chemokines responsible for inflammation and neutrophil recruitment. These data support a beneficial role for, and elucidate the immunological mechanisms underlying, the use of baricitinib as a frontline treatment for inflammation induced by SARS-CoV-2 infection.

Tissue-specific transcriptional profiling of plasmacytoid dendritic cells reveals a hyperactivated state in chronic SIV infection.

HIV associated immune activation (IA) is associated with increased morbidity in people living with HIV (PLWH) on antiretroviral therapy, and remains a barrier for strategies aimed at reducing the HIV reservoir. The underlying mechanisms of IA have not been definitively elucidated, however, persistent production of Type I IFNs and expression of ISGs is considered to be one of the primary factors. Plasmacytoid DCs (pDCs) are a major producer of Type I IFN during viral infections, and are highly immunomodulatory in acute HIV and SIV infection, however their role in chronic HIV/SIV infection has not been firmly established. Here, we performed a detailed transcriptomic characterization of pDCs in chronic SIV infection in rhesus macaques, and in sooty mangabeys, a natural host non-human primate (NHP) species that undergoes non-pathogenic SIV infection. We also investigated the immunostimulatory capacity of lymph node homing pDCs in chronic SIV infection by contrasting gene expression of pDCs isolated from lymph nodes with those from blood. We observed that pDCs in LNs, but not blood, produced high levels of IFNα transcripts, and upregulated gene expression programs consistent with T cell activation and exhaustion. We apply a novel strategy to catalogue uncharacterized surface molecules on pDCs, and identified the lymphoid exhaustion markers TIGIT and LAIR1 as highly expressed in SIV infection. pDCs from SIV-infected sooty mangabeys lacked the activation profile of ISG signatures observed in infected macaques. These data demonstrate that pDCs are a primary producer of Type I IFN in chronic SIV infection. Further, this study demonstrated that pDCs trafficking to LNs persist in a highly activated state well into chronic infection. Collectively, these data identify pDCs as a highly immunomodulatory cell population in chronic SIV infection, and a putative therapeutic target to reduce immune activation.

Calcium-dependent transcriptional changes in human pancreatic islet cells reveal functional diversity in islet cell subtypes.

AIMS/HYPOTHESIS: Pancreatic islets depend on cytosolic calcium (Ca2+) to trigger the secretion of glucoregulatory hormones and trigger transcriptional regulation of genes important for islet response to stimuli. To date, there has not been an attempt to profile Ca2+-regulated gene expression in all islet cell types. Our aim was to construct a large single-cell transcriptomic dataset from human islets exposed to conditions that would acutely induce or inhibit intracellular Ca2+ signalling, while preserving biological heterogeneity. METHODS: We exposed intact human islets from three donors to the following conditions: (1) 2.8 mmol/l glucose; (2) 16 mmol/l glucose and 40 mmol/l KCl to maximally stimulate Ca2+ signalling; and (3) 16 mmol/l glucose, 40 mmol/l KCl and 5 mmol/l EGTA (Ca2+ chelator) to inhibit Ca2+ signalling, for 1 h. We sequenced 68,650 cells from all islet cell types, and further subsetted the cells to form an endocrine cell-specific dataset of 59,373 cells expressing INS, GCG, SST or PPY. We compared transcriptomes across conditions to determine the differentially expressed Ca2+-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells. RESULTS: Based on the number of Ca2+-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of Ca2+ response. We also showed that polyhormonal clusters expressing both INS and GCG, or both INS and SST, are defined by Ca2+-regulated genes specific to each cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of Ca2+-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function. CONCLUSIONS/INTERPRETATION: Here we use our large-scale, multi-condition, single-cell dataset to show that human islets have cell-type-specific Ca2+-regulated gene expression profiles, some of them specific to subpopulations. In our dataset, we identify PCDH7 as a novel marker of beta cells having an increased number of Ca2+-regulated genes and enhanced insulin secretory function. DATA AVAILABILITY: A searchable and user-friendly format of the data in this study, specifically designed for rapid mining of single-cell RNA sequencing data, is available at https://lynnlab.shinyapps.io/Human_Islet_Atlas/ . The raw data files are available at NCBI Gene Expression Omnibus (GSE196715).

Spatial and transcriptional heterogeneity of pancreatic beta cell neogenesis revealed by a time-resolved reporter system.

AIMS/HYPOTHESIS: While pancreatic beta cells have been shown to originate from endocrine progenitors in ductal regions, it remains unclear precisely where beta cells emerge from and which transcripts define newborn beta cells. We therefore investigated characteristics of newborn beta cells extracted by a time-resolved reporter system. METHODS: We established a mouse model, 'Ins1-GFP; Timer', which provides spatial information during beta cell neogenesis with high temporal resolution. Single-cell RNA-sequencing (scRNA-seq) was performed on mouse beta cells sorted by fluorescent reporter to uncover transcriptomic profiles of newborn beta cells. scRNA-seq of human embryonic stem cell (hESC)-derived beta-like cells was also performed to compare newborn beta cell features between mouse and human. RESULTS: Fluorescence imaging of Ins1-GFP; Timer mouse pancreas successfully dissected newly generated beta cells as green fluorescence-dominant cells. This reporter system revealed that, as expected, some newborn beta cells arise close to the ducts (ßduct); unexpectedly, the others arise away from the ducts and adjacent to blood vessels (ßvessel). Single-cell transcriptomic analyses demonstrated five distinct populations among newborn beta cells, confirming spatial heterogeneity of beta cell neogenesis such as high probability of glucagon-positive ßduct, musculoaponeurotic fibrosarcoma oncogene family B (MafB)-positive ßduct and musculoaponeurotic fibrosarcoma oncogene family A (MafA)-positive ßvessel cells. Comparative analysis with scRNA-seq data of mouse newborn beta cells and hESC-derived beta-like cells uncovered transcriptional similarity between mouse and human beta cell neogenesis including microsomal glutathione S-transferase 1 (MGST1)- and synaptotagmin 13 (SYT13)-highly-expressing state. CONCLUSIONS/INTERPRETATION: The combination of time-resolved histological imaging with single-cell transcriptional mapping demonstrated novel features of spatial and transcriptional heterogeneity in beta cell neogenesis, which will lead to a better understanding of beta cell differentiation for future cell therapy. DATA AVAILABILITY: Raw and processed single-cell RNA-sequencing data for this study has been deposited in the Gene Expression Omnibus under accession number GSE155742.

Highly Multiplexed Image Analysis of Intestinal Tissue Sections in Patients With Inflammatory Bowel Disease.

BACKGROUND & AIMS: Significant progress has been made since the first report of inflammatory bowel disease (IBD) in 1859, after decades of research that have contributed to the understanding of the genetic and environmental factors involved in IBD pathogenesis. Today, a range of treatments is available for directed therapy, mostly targeting the overactive immune response. However, the mechanisms by which the immune system contributes to disease pathogenesis and progression are not fully understood. One challenge hindering IBD research is the heterogeneous nature of the disease and the lack of understanding of how immune cells interact with one another in the gut mucosa. Introduction of a technology that enables expansive characterization of the inflammatory environment of human IBD tissues may address this gap in knowledge. METHODS: We used the imaging mass cytometry platform to perform highly multiplex image analysis of IBD and healthy deidentified intestine sections (6 Crohn's disease compared to 6 control ileum; 6 ulcerative colitis compared to 6 control colon). The acquired images were graded for inflammation severity by analysis of adjacent H&E tissue sections. We assigned more than 300,000 cells to unique cell types and performed analyses of tissue integrity, epithelial activity, and immune cell composition. RESULTS: The intestinal epithelia of patients with IBD exhibited increased proliferation rates and expression of HLA-DR compared to control tissues, and both features were positively correlated with the severity of inflammation. The neighborhood analysis determined enrichment of regulatory T cell interactions with CD68+ macrophages, CD4+ T cells, and plasma cells in both forms of IBD, whereas activated lysozyme C+ macrophages were preferred regulatory T cell neighbors in Crohn's disease but not ulcerative colitis. CONCLUSIONS: Altogether, our study shows the power of imaging mass cytometry and its ability to both quantify immune cell types and characterize their spatial interactions within the inflammatory environment by a single analysis platform.

Does the 5-2-1 criteria identify patients with advanced Parkinson's disease? Real-world screening accuracy and burden of 5-2-1-positive patients in 7 countries.

BACKGROUND: The burden of Parkinson's disease (PD) worsens with disease progression. However, the lack of objective and uniform disease classification challenges our understanding of the incremental burden in patients with advanced Parkinson's disease (APD) and suboptimal medication control. The 5-2-1 criteria was proposed by clinical consensus to identify patients with advancing PD. Our objective was to evaluate the screening accuracy and incremental clinical burden, healthcare resource utilization (HCRU), and humanistic burden in PD patients meeting the 5-2-1 screening criteria. METHODS: Data were drawn from the Adelphi Parkinson's Disease Specific Program (DSP™), a multi-country point-in-time survey (2017-2020). People with PD who were naive to device-aided therapy and on oral PD therapy were included. Patients meeting the 5-2-1 screening criteria had one or more of the three clinical indicators of APD: (i) ≥5 doses of oral levodopa/day, OR (ii) "off" symptoms for ≥2 h of waking day, OR (iii) ≥1 h of troublesome dyskinesia. Clinician assessment of PD stage was used as the reference in this study. Clinical screening accuracy of the 5-2-1 criteria was assessed using area under the curve and multivariable logistic regression models. Incremental clinical, HCRU, and humanistic burden were assessed by known-group comparisons between 5 and 2-1-positive and negative patients. RESULTS: From the analytic sample (n = 4714), 33% of patients met the 5-2-1 screening criteria. Among physician-classified APD patients, 78.6% were 5-2-1 positive. Concordance between clinician judgment and 5-2-1 screening criteria was > 75%. 5-2-1-positive patients were nearly 7-times more likely to be classified as APD by physician judgment. Compared with the 5-2-1-negative group, 5-2-1-positive patients had significantly higher clinical, HCRU, and humanistic burden across all measures. In particular, 5-2-1-positive patients had 3.8-times more falls, 3.6-times higher annual hospitalization rate, and 3.4-times greater dissatisfaction with PD treatment. 5-2-1-positive patients also had significantly lower quality of life and worse caregiver burden. CONCLUSIONS: 5-2-1 criteria demonstrated potential as a screening tool for identifying people with APD with considerable clinical, humanistic, and HCRU burden. The 5-2-1 screening criteria is an objective and reliable tool that may aid the timely identification and treatment optimization of patients inadequately controlled on oral PD medications.

HIV latency reversing agents act through Tat post translational modifications.

BACKGROUND: Different classes of latency reversing agents (LRAs) are being evaluated to measure their effects in reactivating HIV replication from latently infected cells. A limited number of studies have demonstrated additive effects of LRAs with the viral protein Tat in initiating transcription, but less is known about how LRAs interact with Tat, particularly through basic residues that may be post-translationally modified to alter the behaviour of Tat for processive transcription and co-transcriptional RNA processing. RESULTS: Here we show that various lysine and arginine mutations reduce the capacity of Tat to induce both transcription and mRNA splicing. The lysine 28 and lysine 50 residues of Tat, or the acetylation and methylation modifications of these basic amino acids, were essential for Tat transcriptional control, and also for the proviral expression effects elicited by histone deacetylase inhibitors (HDACi) or the bromodomain inhibitor JQ1. We also found that JQ1 was the only LRA tested that could induce HIV mRNA splicing in the absence of Tat, or rescue splicing for Tat lysine mutants in a BRD4-dependent manner. CONCLUSIONS: Our data provide evidence that Tat activities in both co-transcriptional RNA processing together with transcriptional initiation and processivity are crucial during reactivation of latent HIV infection. The HDACi and JQ1 LRAs act with Tat to increase transcription, but JQ1 also enables post-transcriptional mRNA splicing. Tat residues K28 and K50, or their modifications through acetylation or methylation, are critical for LRAs that function in conjunction with Tat.

The Molecular Biology of HIV Latency.

HIV remains incurable due to the existence of a reservoir of cells that harbor intact integrated genomes of the virus in the absence of viral replication. This population of infected cells remains invisible to the immune system and is not targeted by the drugs used in the current antiretroviral therapies (cART). Reversal of latency by the use of inhibitors of chromatin-remodeling enzymes has been studied extensively in an attempt to purge this reservoir of latent HIV but has thus far not shown any success in clinical trials. The full complexity of latent HIV infection has still not been appreciated, and the gaps in knowledge prevent development of adequate small-molecule compounds that can effectively perturb this reservoir. In this review, we will examine the role of epigenetic silencing of HIV transcription, posttranscriptional regulation, and mRNA processing in promoting HIV-1 latency.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) enables the quantification of chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types/states. However, when analyzed individually, scRNA-seq and snATAC-seq data often produce conflicting results regarding cell type/state assignment. In addition, there is a loss of power as the two modalities reflect the same underlying cell types/states. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data make it possible to directly model the relationships between the two modalities. However, given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality data to gain a comprehensive view of the cellular complexity. Here, we benchmarked the performance of seven existing single-cell multi-omic data integration methods. Specifically, we evaluated whether these methods are able to uncover peak-gene associations from single-modality data, and to what extent the multiome data can provide additional guidance for the analysis of the existing single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data, but the number of cells in the multiome data is critical to ensure a good cell type annotation. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. Lastly, Seurat v4 is the best at integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity. RESULTS: We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. CONCLUSIONS: Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

TREM2+ and interstitial-like macrophages orchestrate airway inflammation in SARS-CoV-2 infection in rhesus macaques.

The immunopathological mechanisms driving the development of severe COVID-19 remain poorly defined. Here, we utilize a rhesus macaque model of acute SARS-CoV-2 infection to delineate perturbations in the innate immune system. SARS-CoV-2 initiates a rapid infiltration of plasmacytoid dendritic cells into the lower airway, commensurate with IFNA production, natural killer cell activation, and a significant increase of blood CD14-CD16+ monocytes. To dissect the contribution of lung myeloid subsets to airway inflammation, we generate a longitudinal scRNA-Seq dataset of airway cells, and map these subsets to corresponding populations in the human lung. SARS-CoV-2 infection elicits a rapid recruitment of two macrophage subsets: CD163+MRC1-, and TREM2+ populations that are the predominant source of inflammatory cytokines. Treatment with baricitinib (Olumiant®), a JAK1/2 inhibitor is effective in eliminating the influx of non-alveolar macrophages, with a reduction of inflammatory cytokines. This study delineates the major lung macrophage subsets driving airway inflammation during SARS-CoV-2 infection.

A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation.

CITE-seq, a single-cell multi-omics technology that measures RNA and protein expression simultaneously in single cells, has been widely applied in biomedical research, especially in immune related disorders and other diseases such as influenza and COVID-19. Despite the proliferation of CITE-seq, it is still costly to generate such data. Although data integration can increase information content, this raises computational challenges. First, combining multiple datasets is prone to batch effects that need to be addressed. Secondly, it is difficult to combine multiple CITE-seq datasets because the protein panels in different datasets may only partially overlap. Integrating multiple CITE-seq and single-cell RNA-seq (scRNA-seq) datasets is important because this allows the utilization of as many data as possible to uncover cell population heterogeneity. To overcome these challenges, we present sciPENN, a multi-use deep learning approach that supports CITE-seq and scRNA-seq data integration, protein expression prediction for scRNA-seq, protein expression imputation for CITE-seq, quantification of prediction and imputation uncertainty, and cell type label transfer from CITE-seq to scRNA-seq. Comprehensive evaluations spanning multiple datasets demonstrate that sciPENN outperforms other current state-of-the-art methods.

Detection of Chimeric Cellular: HIV mRNAs Generated Through Aberrant Splicing in HIV-1 Latently Infected Resting CD4+ T Cells.

Latent HIV-1 provirus in infected individuals on suppressive therapy does not always remain transcriptionally silent. Both HIV-1 LTR and human gene promoter derived transcriptional events can contribute HIV-1 sequences to the mRNA produced in the cell. In addition, chimeric cellular:HIV mRNA can arise through readthrough transcription and aberrant splicing. Using target enrichment coupled to the Illumina Mi-Seq and PacBio RS II platforms, we show that 3' LTR activation is frequent in latently infected cells from both the CCL19-induced primary cell model of HIV-1 latency as well as ex vivo samples. In both systems of latent HIV-1 infection, we detected several chimeric species that were generated via activation of a cryptic splice donor site in the 5' LTR of HIV-1. Aberrant splicing involving the major HIV-1 splice donor sites, SD1 and SD4 disrupts post-transcriptional processing of the gene in which HIV-1 is integrated. In the primary cell model of HIV-1 latency, Tat-encoding sequences are incorporated into the chimeric mRNA transcripts through the use of SD4. Our study unravels clues to the characteristics of HIV-1 integrants that promote formation of chimeric cellular:HIV mRNA and improves the understanding of the HIV-1 RNA footprint in latently infected cells.

Single-cell analysis of the human pancreas in type 2 diabetes using multi-spectral imaging mass cytometry.

Type 2 diabetes mellitus (T2D) is a chronic age-related disorder characterized by hyperglycemia due to the failure of pancreatic beta cells to compensate for increased insulin demand. Despite decades of research, the pathogenic mechanisms underlying T2D remain poorly defined. Here, we use imaging mass cytometry (IMC) with a panel of 34 antibodies to simultaneously quantify markers of pancreatic exocrine, islet, and immune cells and stromal components. We analyze over 2 million cells from 16 pancreata obtained from donors with T2D and 13 pancreata from age-similar non-diabetic controls. In the T2D pancreata, we observe significant alterations in islet architecture, endocrine cell composition, and immune cell constituents. Thus, both HLA-DR-positive CD8 T cells and macrophages are enriched intra-islet in the T2D pancreas. These efforts demonstrate the utility of IMC for investigating complex events at the cellular level in order to provide insights into the pathophysiology of T2D.

The RNA-Binding Proteins SRP14 and HMGB3 Control HIV-1 Tat mRNA Processing and Translation During HIV-1 Latency.

HIV-1 Tat protein is essential for virus production. RNA-binding proteins that facilitate Tat production may be absent or downregulated in resting CD4+ T-cells, the main reservoir of latent HIV in people with HIV (PWH) on antiretroviral therapy (ART). In this study, we examined the role of Tat RNA-binding proteins on the expression of Tat and control of latent and productive infection. Affinity purification coupled with mass spectrometry analysis was used to detect binding partners of MS2-tagged tat mRNA in a T cell-line model of HIV latency. The effect of knockdown and overexpression of the proteins of interest on Tat transactivation and translation was assessed by luciferase-based reporter assays and infections with a dual color HIV reporter virus. Out of the 243 interactions identified, knockdown of SRP14 (Signal Recognition Particle 14) negatively affected tat mRNA processing and translation as well as Tat-mediated transactivation, which led to an increase in latent infection. On the other hand, knockdown of HMGB3 (High Mobility Group Box 3) resulted in an increase in Tat transactivation and translation as well as an increase in productive infection. Footprinting experiments revealed that SRP14 and HMGB3 proteins bind to TIM-TAM, a conserved RNA sequence-structure in tat mRNA that functions as a Tat IRES modulator of tat mRNA. Overexpression of SRP14 in resting CD4+ T-cells from patients on ART was sufficient to reverse HIV-1 latency and induce virus production. The role of SRP14 and HMGB3 proteins in controlling HIV Tat expression during latency will be further assessed as potential drug targets.

TREM2+ and interstitial macrophages orchestrate airway inflammation in SARS-CoV-2 infection in rhesus macaques.

The COVID-19 pandemic remains a global health crisis, yet, the immunopathological mechanisms driving the development of severe disease remain poorly defined. Here, we utilize a rhesus macaque (RM) model of SARS-CoV-2 infection to delineate perturbations in the innate immune system during acute infection using an integrated systems analysis. We found that SARS-CoV-2 initiated a rapid infiltration (two days post infection) of plasmacytoid dendritic cells into the lower airway, commensurate with IFNA production, natural killer cell activation, and induction of interferon-stimulated genes. At this early interval, we also observed a significant increase of blood CD14-CD16+ monocytes. To dissect the contribution of lung myeloid subsets to airway inflammation, we generated a novel compendium of RM-specific lung macrophage gene expression using a combination of sc-RNA-Seq data and bulk RNA-Seq of purified populations under steady state conditions. Using these tools, we generated a longitudinal sc-RNA-seq dataset of airway cells in SARS-CoV-2-infected RMs. We identified that SARS-CoV-2 infection elicited a rapid recruitment of two subsets of macrophages into the airway: a C206+MRC1-population resembling murine interstitial macrophages, and a TREM2+ population consistent with CCR2+ infiltrating monocytes, into the alveolar space. These subsets were the predominant source of inflammatory cytokines, accounting for ~75% of IL6 and TNF production, and >90% of IL10 production, whereas the contribution of CD206+MRC+ alveolar macrophages was significantly lower. Treatment of SARS-CoV-2 infected RMs with baricitinib (Olumiant ® ), a novel JAK1/2 inhibitor that recently received Emergency Use Authorization for the treatment of hospitalized COVID-19 patients, was remarkably effective in eliminating the influx of infiltrating, non-alveolar macrophages in the alveolar space, with a concomitant reduction of inflammatory cytokines. This study has delineated the major subsets of lung macrophages driving inflammatory and anti-inflammatory cytokine production within the alveolar space during SARS-CoV-2 infection. ONE SENTENCE SUMMARY: Multi-omic analyses of hyperacute SARS-CoV-2 infection in rhesus macaques identified two population of infiltrating macrophages, as the primary orchestrators of inflammation in the lower airway that can be successfully treated with baricitinib.