The insatiable appetite for energy to support human brain function is mainly supplied by glucose oxidation (CMR). But how much energy is consumed for signaling and nonsignaling processes in gray/white matter is highly debated. We examined this issue by combining metabolic measurements of gray/white matter and a theoretical calculation of bottom-up energy budget using biophysical properties of neuronal/glial cells in conjunction with species-exclusive electrophysiological and morphological data. We calculated a CMR-derived budget and confirmed it with experimental results measured by PET, autoradiography, C-MRS, and electrophysiology. Several conserved principles were observed regarding the energy costs for brain's signaling and nonsignaling components in both human and rat. The awake resting cortical signaling processes and mass-dependent nonsignaling processes, respectively, demand ∼70% and ∼30% of CMR. Inhibitory neurons and glia need 15-20% of CMR, with the rest demanded by excitatory neurons. Nonsignaling demands dominate in white matter, in near opposite contrast to gray matter demands. Comparison between C-MRS data and calculations suggests ∼1.2 Hz glutamatergic signaling rate in the awake human cortex, which is ∼4 times lower than signaling in the rat cortex. Top-down validated bottom-up budgets could allow computation of anatomy-based CMR maps and accurate cellular level interpretation of brain metabolic imaging.

The functional properties of circulating CD8+ T cells have been associated with immune control of HIV. However, viral replication occurs predominantly in secondary lymphoid tissues, such as lymph nodes (LNs). We used an integrated single-cell approach to characterize effective HIV-specific CD8+ T cell responses in the LNs of elite controllers (ECs), defined as individuals who suppress viral replication in the absence of antiretroviral therapy (ART). Higher frequencies of total memory and follicle-homing HIV-specific CD8+ T cells were detected in the LNs of ECs compared with the LNs of chronic progressors (CPs) who were not receiving ART. Moreover, HIV-specific CD8+ T cells potently suppressed viral replication without demonstrable cytolytic activity in the LNs of ECs, which harbored substantially lower amounts of CD4+ T cell–associated HIV DNA and RNA compared with the LNs of CPs. Single-cell RNA sequencing analyses further revealed a distinct transcriptional signature among HIV-specific CD8+ T cells from the LNs of ECs, typified by the down-regulation of inhibitory receptors and cytolytic molecules and the up-regulation of multiple cytokines, predicted secreted factors, and components of the protein translation machinery. Collectively, these results provide a mechanistic framework to expedite the identification of novel antiviral factors, highlighting a potential role for the localized deployment of noncytolytic functions as a determinant of immune efficacy against HIV.

SUMMARY Complement dysregulation is a feature of many retinal diseases, yet mechanistic understanding at the cellular level is limited. Given this knowledge gap about which retinal cells express complement, we performed single-cell RNA sequencing on ~92,000 mouse retinal cells and validated our results in five major purified retinal cell types. We found evidence for a distributed cell-type-specific complement expression across 11 cell types. Notably, Müller cells are the major contributor of complement activators c1s, c3, c4, and cfb. Retinal pigment epithelium (RPE) mainly expresses cfh and the terminal complement components, whereas cfi and cfp transcripts are most abundant in neurons. Aging enhances c1s, cfb, cfp, and cfi expression, while cfh expression decreases. Transient retinal ischemia increases complement expression in microglia, Müller cells, and RPE. In summary, we report a unique complement expression signature for murine retinal cell types suggesting a well-orchestrated regulation of local complement expression in the retinal microenvironment.

Vaccine-mediated immunity often relies on the generation of protective antibodies and memory B cells, which commonly stem from germinal center (GC) reactions. An in-depth comparison of the GC responses elicited by SARS-CoV-2 mRNA vaccines in healthy and immunocompromised individuals has not yet been performed due to the challenge of directly probing human lymph nodes. Herein, through a fine-needle-aspiration-based approach, we profiled the immune responses to SARS-CoV-2 mRNA vaccines in lymph nodes of healthy individuals and kidney transplant recipients (KTXs). We found that, unlike healthy subjects, KTXs presented deeply blunted SARS-CoV-2-specific GC B cell responses coupled with severely hindered T follicular helper cell, SARS-CoV-2 receptor-binding-domain-specific memory B cell and neutralizing antibody responses. KTXs also displayed reduced SARS-CoV-2-specific CD4 and CD8 T cell frequencies. Broadly, these data indicate impaired GC-derived immunity in immunocompromised individuals, and suggest a GC-origin for certain humoral and memory B cell responses following mRNA vaccination.

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.In scRNA-seq studies, technical noise blurs precise distinctions between cell states, and genes with low expression cannot be accurately quantified. Existing methods [1][2][3][4][5][6] to denoise scRNA-seq data often underperform when sequencing depth is low or when the cell type of interest is rare, and also ignore datasets in public domain, which may contain relevant information to aid denoising. Ensuing the mouse cell atlases 7,8 , we will soon have detailed atlases for each anatomic organ in the human body 9 . Publicly available scRNA-seq datasets contain information about cell types and gene signatures that is relevant to newly generated data. Yet, it is unclear how to borrow information across platforms, subjects and tissues. Moreover, such transfer learning must not introduce bias or force the new data to lose its distinctive features.Here, we describe a denoising method, Single-cell Analysis via Expression Recovery harnessing eXternal data (SAVER-X), which couples a Bayesian hierarchical model to a pretrainable deep autoencoder 10 . Although neural networks have formed the basis of other *

The intrahepatic milieu is inhospitable to intraportal islet allografts 1 – 3 , limiting their applicability for the treatment of Type 1 Diabetes (T1D). Although the subcutaneous space represents an alternate, safe and easily accessible site for pancreatic islet transplantation, lack of neovascularization and the resulting hypoxic cell death have largely limited the longevity of graft survival and function, and pose a barrier for the widespread adoption of islet transplantation in the clinic. Here we report the successful subcutaneous transplantation of pancreatic islets admixed with a device-free Islet Viability Matrix (IVM), resulting in long-term euglycemia in diverse immune-competent and immuno-incompetent animal models. We validate sustained normoglycemia afforded by our transplantation methodology using murine, porcine and human pancreatic islets, and also demonstrate its efficacy in a nonhuman primate model of syngeneic islet transplantation. Transplantation of the islet–IVM mixture in the subcutaneous space represents a simple, safe and reproducible method, paving the way for a new therapeutic paradigm for T1D.

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer 21 learning across datasets remarkably improves data quality. By coupling a deep autoencoder 22

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