# Optimizing methods of clinical sample processing for scRNA-seq and mechanistic studies in sepsis to enable reliable, reproducible, and high-yield multi-center collection efforts > **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $199,629 ## Abstract Project summary: Sepsis is prevalent, costly, and deadly. In the U.S, sepsis accounts for 4% of hospitalizations, 13% of in-hospital healthcare expenditures, and 35% of in-hospital deaths. Although common, sepsis is often difficult to diagnose and treat effectively, since it is a syndrome defined generically by organ dysfunction resulting from a dysregulated immune response to infection. This broad definition leads to heterogeneity of disease and misdiagnosis. Clinical trials thus enroll ill-characterized populations of sepsis patients with variable underlying immune responses to infection, diluting the effects of novel and otherwise promising therapies that might benefit a defined subset of patients. Precision immune cell-specific characterization of the dysregulated host immune response in sepsis is clearly needed. Our group was the first to elucidate an immunosuppressive monocyte substate (MS1) expanded in patients with urosepsis (Reyes et al. Nat Med 2020) using single cell RNA sequencing (scRNA-seq), which resolves cellular heterogeneity, revealing rich signatures of immune cell-specific gene expression not evident from standard immune profiling techniques. Clinical investigation in sepsis would greatly benefit from the scalable deployment of scRNA-seq across multicenter studies and clinical trials to enable robust characterization of the host immune response in sepsis, and to explain the effect of disease heterogeneity on clinical course, outcomes, and treatment effects. Yet in order to facilitate subsequent scRNA-seq, immune cells must be isolated from whole blood samples, which currently involves complex real-time processing of fresh blood at clinical sites, a process neither practical nor reproducible enough to allow deployment across multiple clinical centers. To address this critical need, we piloted a simple method of onsite whole blood cryopreservation that uses only 2 mL of blood and is easily deployable at clinical sites, followed by storage for scRNA-seq at a centralized center of expertise. In the R21 phase, we will optimize and validate our method of whole blood cryopreservation on enrolled subjects at 2 local clinical sites versus gold standard methods for immune cell isolation. We will compare scRNA-seq technical quality metrics and biologically-relevant measures including the MS1 monocyte subtype and other immune cell states. Importantly, we will test the viability and function of cryopreserved cells in mechanistic studies. In the R33 phase, we will scale our whole blood cryopreservation method to 5 enrolling clinical sites around the U.S. to demonstrate feasibility of a multicenter collection with centralized scRNA-seq and analysis. In this expanded sepsis cohort, we will perform deep immune profiling and derive scRNA-seq- based endotypes to characterize underlying heterogeneity of host immune responses. We will compare these endotypes to those derived from bulk RNA sequencing and apply scRNA-seq-derived signatures to our ow... ## Key facts - **NIH application ID:** 10756163 - **Project number:** 5R21GM148826-02 - **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL - **Principal Investigator:** ROBY PAUL BHATTACHARYYA - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $199,629 - **Award type:** 5 - **Project period:** 2023-01-01 → 2024-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10756163 ## Citation > US National Institutes of Health, RePORTER application 10756163, Optimizing methods of clinical sample processing for scRNA-seq and mechanistic studies in sepsis to enable reliable, reproducible, and high-yield multi-center collection efforts (5R21GM148826-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10756163. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*