Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex.

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response.

T cells mediate antigen-specific immune responses to disease through the specificity and diversity of their clonotypic T cell receptors (TCRs). Determining the spatial distributions of T cell clonotypes in tissues is essential to understanding T cell behavior, but spatial sequencing methods remain unable to profile the TCR repertoire. Here, we developed Slide-TCR-seq, a 10-µm-resolution method, to sequence whole transcriptomes and TCRs within intact tissues. We confirmed the ability of Slide-TCR-seq to map the characteristic locations of T cells and their receptors in mouse spleen. In human lymphoid germinal centers, we identified spatially distinct TCR repertoires. Profiling T cells in renal cell carcinoma and melanoma specimens revealed heterogeneous immune responses: T cell states and infiltration differed intra- and inter-clonally, and adjacent tumor and immune cells exhibited distinct gene expression. Altogether, our method yields insights into the spatial relationships between clonality, neighboring cell types, and gene expression that drive T cell responses.

Simple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems.

Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.

The molecular cytoarchitecture of the adult mouse brain.

The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq1,2-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource ( www.BrainCellData.org ), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases.

An atlas of healthy and injured cell states and niches in the human kidney.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

Cell type-specific inference of differential expression in spatial transcriptomics.

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .

Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF.

BACKGROUND: Heart Failure with Preserved Ejection Fraction (HFpEF) is a heterogenous disease with few therapies proven to provide clinical benefit. Machine learning can characterize distinct phenotypes and compare outcomes among patients with HFpEF who are hospitalized for acute HF. METHODS: We applied hierarchical clustering using demographics, comorbidities, and clinical data on admission to identify distinct clusters in hospitalized HFpEF (ejection fraction >40%) in the ASCEND-HF trial. We separately applied a previously developed latent class analysis (LCA) clustering method and compared in-hospital and long-term outcomes across cluster groups. RESULTS: Of 7141 patients enrolled in the ASCEND-HF trial, 812 (11.4%) were hospitalized for HFpEF and met the criteria for complete case analysis. Hierarchical Cluster 1 included older women with atrial fibrillation (AF). Cluster 2 had elevated resting blood pressure. Cluster 3 had young men with obesity and diabetes. Cluster 4 had low resting blood pressure. Mortality at 180 days was lowest among Cluster 3 (KM event-rate 6.2 [95% CI: 3.5, 10.9]) and highest among Cluster 4 (18.8 [14.6, 24.0], P < .001). Twenty four-hour urine output was higher in Cluster 3 (2700 mL [1800, 3975]) than Cluster 4 (2100 mL [1400, 3055], P < .001). LCA also identified four clusters: A) older White or Asian women, B) younger men with few comorbidities, C) older individuals with AF and renal impairment, and D) patients with obesity and diabetes. Mortality at 180 days was lowest among LCA Cluster B (KM event-rate 5.5 [2.0, 10.3]) and highest among LCA Cluster C (26.3 [19.2, 35.4], P < .001). CONCLUSIONS: In patients hospitalized for HFpEF, cluster analysis demonstrated distinct phenotypes with differing clinical profiles and outcomes.

Stochastic electrotransport selectively enhances the transport of highly electromobile molecules.

Nondestructive chemical processing of porous samples such as fixed biological tissues typically relies on molecular diffusion. Diffusion into a porous structure is a slow process that significantly delays completion of chemical processing. Here, we present a novel electrokinetic method termed stochastic electrotransport for rapid nondestructive processing of porous samples. This method uses a rotational electric field to selectively disperse highly electromobile molecules throughout a porous sample without displacing the low-electromobility molecules that constitute the sample. Using computational models, we show that stochastic electrotransport can rapidly disperse electromobile molecules in a porous medium. We apply this method to completely clear mouse organs within 1-3 days and to stain them with nuclear dyes, proteins, and antibodies within 1 day. Our results demonstrate the potential of stochastic electrotransport to process large and dense tissue samples that were previously infeasible in time when relying on diffusion.

Retrospective Analysis of the Short-Term Safety of ECT in Patients With Neurological Comorbidities: A Guide for Pre-ECT Neurological Evaluations.

Pre-ECT neurology consultations are often requested to determine the relative risk of the procedure in patients with neurological comorbidities, but there is limited data to guide clinicians. The authors performed a retrospective chart review of all consecutive inpatients at McLean Hospital who underwent a pre-ECT neurological evaluation between January 2012 and June 2014 (N=68). ECT was safe and effective in patients with a wide variety of neurological diseases. Only one minor event was related to a neurological comorbidity, and there were no serious neurological complications. Based on the latest evidence, the authors provide guidance on the pre-ECT evaluation with respect to neurologic status.

Detection of allele-specific expression in spatial transcriptomics with spASE.

Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.

Effects of Sacubitril/Valsartan vs Valsartan in De Novo vs Acute on Chronic HFpEF and HFmrEF.

Background: Decompensated heart failure (HF) can be categorized as de novo or worsening of chronic HF. In PARAGLIDE-HF (Prospective comparison of ARNI with ARB Given following stabiLization In DEcompensated HFpEF), among patients with an ejection fraction >40% that stabilized after worsening HF, sacubitril/valsartan led to a significantly greater reduction in N-terminal pro-B-type natriuretic peptide (NT-proBNP) and was associated with clinical benefit compared to valsartan. Objectives: This prespecified analysis characterized patients with de novo vs worsening chronic HF in PARAGLIDE-HF and assessed the interaction between HF chronicity and the effect of sacubitril/valsartan. Methods: Patients were classified as de novo (first diagnosis of HF) or chronic (known HF prior to the index event). Time-averaged proportional change in NT-proBNP from baseline to weeks 4 and 8 was analyzed using an analysis of covariance model. A win ratio consisting of time to cardiovascular death, number and times of HF hospitalizations during follow-up, number and times of urgent HF visits during follow-up, and time-averaged proportional change in NT-proBNP was assessed for each group. Results: Of the 466 participants, 153 (33%) had de novo HF and 313 (67%) had chronic HF. De novo patients had lower rates of atrial fibrillation/flutter and lower creatinine. There was a nonsignificant reduction in NT-proBNP with sacubitril/valsartan vs valsartan for de novo (0.82; 95% CI: 0.62-1.07) and chronic HF (0.88; 95% CI: 0.73-1.07), interaction P = 0.66. The win ratio was nominally in favor of sacubitril/valsartan for both de novo (1.12; 95% CI: 0.70-1.58) and chronic HF (1.24; 95% CI: 0.89-1.71). Conclusions: There is no interaction between HF chronicity and the effect of sacubitril-valsartan.

CD4+ T cells display a spectrum of recall dynamics during re-infection with malaria parasites.

Children in malaria-endemic regions can experience repeated Plasmodium infections over short periods of time. Effects of re-infection on multiple co-existing CD4+ T cell subsets remain unresolved. Here, we examine antigen-experienced CD4+ T cells during re-infection in mice, using scRNA-seq/TCR-seq and spatial transcriptomics. TCR transgenic TEM cells initiate rapid Th1/Tr1 recall responses prior to proliferating, while GC Tfh counterparts are refractory, with TCM/Tfh-like cells exhibiting modest non-proliferative responses. Th1-recall is a partial facsimile of primary Th1-responses, with no upregulated effector-associated genes being unique to recall. Polyclonal, TCR-diverse, CD4+ T cells exhibit similar recall dynamics, with individual clones giving rise to multiple effectors including highly proliferative Th1/Tr1 cells, as well as GC Tfh and Tfh-like cells lacking proliferative capacity. Thus, we show substantial diversity in recall responses mounted by multiple co-existing CD4+ T cell subsets in the spleen, and present graphical user interfaces for studying gene expression dynamics and clonal relationships during re-infection.

Plasmodium infection induces phenotypic, clonal, and spatial diversity among differentiating CD4+ T cells.

Naive CD4+ T cells must differentiate in order to orchestrate immunity to Plasmodium, yet understanding of their emerging phenotypes, clonality, spatial distributions, and cellular interactions remains incomplete. Here, we observe that splenic polyclonal CD4+ T cells differentiate toward T helper 1 (Th1) and T follicular helper (Tfh)-like states and exhibit rarer phenotypes not elicited among T cell receptor (TCR) transgenic counterparts. TCR clones present at higher frequencies exhibit Th1 skewing, suggesting that variation in major histocompatibility complex class II (MHC-II) interaction influences proliferation and Th1 differentiation. To characterize CD4+ T cell interactions, we map splenic microarchitecture, cellular locations, and molecular interactions using spatial transcriptomics at near single-cell resolution. Tfh-like cells co-locate with stromal cells in B cell follicles, while Th1 cells in red pulp co-locate with activated monocytes expressing multiple chemokines and MHC-II. Spatial mapping of individual transcriptomes suggests that proximity to chemokine-expressing monocytes correlates with stronger effector phenotypes in Th1 cells. Finally, CRISPR-Cas9 gene disruption reveals a role for CCR5 in promoting clonal expansion and Th1 differentiation. A database of cellular locations and interactions is presented: https://haquelab.mdhs.unimelb.edu.au/spatial_gui/.

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity.

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

The clinical significance of bilateral basal ganglia calcification presenting with mania and delusions.

The authors present the case of a 37-year-old man who developed a psychotic manic episode and was found to have bilateral basal ganglia calcification (BGC). The authors present this case report along with a discussion of the literature on the neuropsychiatry of BGC.

TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics.

Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations).

Spatiotemporal transcriptomic maps of whole mouse embryos at the onset of organogenesis.

Spatiotemporal orchestration of gene expression is required for proper embryonic development. The use of single-cell technologies has begun to provide improved resolution of early regulatory dynamics, including detailed molecular definitions of most cell states during mouse embryogenesis. Here we used Slide-seq to build spatial transcriptomic maps of complete embryonic day (E) 8.5 and E9.0, and partial E9.5 embryos. To support their utility, we developed sc3D, a tool for reconstructing and exploring three-dimensional 'virtual embryos', which enables the quantitative investigation of regionalized gene expression patterns. Our measurements along the main embryonic axes of the developing neural tube revealed several previously unannotated genes with distinct spatial patterns. We also characterized the conflicting transcriptional identity of 'ectopic' neural tubes that emerge in Tbx6 mutant embryos. Taken together, we present an experimental and computational framework for the spatiotemporal investigation of whole embryonic structures and mutant phenotypes.

In-Hospital Virtual Peer-to-Peer Consultation to Increase Guideline-Directed Medical Therapy for Heart Failure: A Pilot Randomized Trial.

BACKGROUND: Guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) improves clinical outcomes and quality of life. Optimizing GDMT in the hospital is associated with greater long-term use in HFrEF. This study aimed to describe the efficacy of a multidisciplinary virtual HF intervention on GDMT optimization among patients with HFrEF admitted for any cause. METHODS: In this pilot randomized, controlled study, consecutive patients with HFrEF admitted to noncardiology medicine services for any cause were identified at a large academic tertiary care hospital between May to September 2021. Major exclusions were end-stage renal disease, hemodynamic instability, concurrent COVID-19 infection, and current enrollment in hospice care. Patients were randomized to a clinician-level virtual peer-to-peer consult intervention providing GDMT recommendations and information on medication costs versus usual care. Primary end points included (1) proportion of patients with new GDMT initiation or use and (2) changes to HF optimal medical therapy scores which included target dosing (range, 0-9). RESULTS: Of 242 patients identified, 91 (38%) were eligible and randomized to intervention (N=52) or usual care (N=39). Baseline characteristics were similar between intervention and usual care (mean age 63 versus 67 years, 23% versus 26% female, 46% versus 49% Black, mean ejection fraction 33% versus 31%). GDMT use on admission was also similar. There were greater proportions of patients with GDMT initiation or continuation with the intervention compared with usual care. After adjusting for optimal medical therapy score on admission, changes to optimal medical therapy score at discharge were higher for the intervention group compared with usual care (+0.44 versus -0.31, absolute difference +0.75, adjusted estimate 0.86±0.42; P=0.041). CONCLUSIONS: Among eligible patients with HFrEF hospitalized for any cause on noncardiology services, a multidisciplinary pilot virtual HF consultation increased new GDMT initiation and dose optimization at discharge.

Reactive organic carbon air emissions from mobile sources in the United States.

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States. We update existing methods for calculating mobile source organic particle and vapor emissions in the United States with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from on-road vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from Teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate-volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of US mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20 %-25 % of the IVOC concentrations and 4.4 %-21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the United States (4.3 %-11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.