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Pluripotent mouse embryonic stem cells (ESCs) can differentiate to all germ layers and serve as an in vitro model of embryonic development. To better understand the differentiation paths traversed by ESCs committing to different lineages, we track individual differentiating ESCs by timelapse imaging followed by multiplexed high-dimensional Imaging Mass Cytometry (IMC) protein quantification. This links continuous live single-cell molecular NANOG and cellular dynamics quantification over 5-6 generations to protein expression of 37 different molecular regulators in the same single cells at the observation endpoints. Using this unique data set including kinship history and live lineage marker detection, we show that NANOG downregulation occurs generations prior to, but is not sufficient for neuroectoderm marker Sox1 upregulation. We identify a developmental cell type co-expressing both the canonical Sox1 neuroectoderm and FoxA2 endoderm markers in vitro and confirm the presence of such a population in the post-implantation embryo. RNASeq reveals cells co-expressing SOX1 and FOXA2 to have a unique cell state characterized by expression of both endoderm as well as neuroectoderm genes suggesting lineage potential towards both germ layers., (© 2024. The Author(s).)

Background: Enhanced recovery after surgery (ERAS) protocols are evidence-based, multimodal approaches to optimize patient recovery and minimize complications., Objectives: Our team evaluated clinical outcomes following the implementation of an ERAS protocol for adolescents undergoing metabolic and bariatric surgery., Setting: Academic hospital, New York, NY, USA., Methods: We performed a single-institution longitudinal assessment of adolescents who underwent laparoscopic vertical sleeve gastrectomy (VSG) between August 2021 and November 2022. Unpaired t-tests and Fisher's exact test were used to compare means between groups and categorical factors., Results: Forty-three patients were included in the study, 21 who participated in the ERAS protocol and 22 control patients. ERAS cohort was 52% females, with a median age of 17.5 years and a median body mass index (BMI) of 46.3 kg/m 2 . The non-ERAS cohort was 59% females, with a median age of 16.7 years and a median BMI of 44.0 kg/m 2 . There were no significant differences between baseline characteristics. Patients in the ERAS group had a shorter time to oral intake (10.7 hours versus 21.5 hours, P < .01), lower morphine milligram equivalents (18.2 versus 97.0, P < .01), and shorter length of stay (1.5 days versus 2.0 days, P = .01). There were no significant differences between return visits to the emergency department (ED) within 30 days (3 versus 2, P = .66) or readmissions (0 versus 1, P = 1.0)., Conclusions: The described ERAS protocol is safe and effective in adolescents undergoing laparoscopic VSG and is associated with shorter time to oral intake, reduced opioid requirements, and shorter hospital lengths of stay with no increase in return ED visits or readmissions., (Copyright © 2024 American Society for Metabolic and Bariatric Surgery. Published by Elsevier Inc. All rights reserved.)

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI , a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI , single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R 2 of 0.87 and a Pearson's correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R 2 of 0.90, and a Pearson's correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

Introduction: Weight loss has been identified as a key strategy for improving glycemic and metabolic outcomes in people with type 2 diabetes (T2D). However, the long-term, real-world impact of weight loss on these outcomes remains unclear. This study aimed to investigate (1) the association between weight loss and glycemic control, (2) association between weight loss and metabolic parameters, and (3) predictors of weight loss and how weight change trajectory varies based on index body mass index (BMI)., Methods: A retrospective, longitudinal cohort study using the linked IQVIA Ambulatory electronic medical records and PharMetrics ® Plus databases was performed from January 1, 2010 through December 31, 2019 in adults with T2D. Participants were categorized into 1-year and 5-year follow-up cohorts based on their observed weight change over time. Longitudinal values for vital signs and laboratory parameters, including BMI, weight, glycated hemoglobin (HbA1c), and metabolic parameters (liver enzymes and cholesterol), were reported at index date and every 6 months post index date. Multivariable logistic regression analysis was used to evaluate the factors associated with weight loss., Results: Of 1,493,964 people evaluated, 1,061,354 (71%) and 308,320 (20.6%) were classified into the 1-year and 5-year follow-up cohorts. Average HbA1c reductions of 1.2% and 0.5% were observed among people who lost ≥ 15% of index weight in the 1-year and 5-year follow-up cohorts, respectively. Higher weight loss percentages were associated with numerically greater improvements in metabolic parameters. The presence of bariatric surgery and higher index BMIs were identified as the strongest predictors of ≥ 15% and ≥ 10% weight loss in both follow-up cohorts., Conclusion: Results from this study suggest that modest and sustained weight loss can lead to clinically meaningful improvements in glycemic and metabolic parameters among people with T2D. These findings highlight the importance of weight management in managing T2D and preventing its associated complications., (© 2023. The Author(s).)

Introduction: Basal insulin's position in the type 2 diabetes (T2D) treatment paradigm has undergone significant revisions since the advent of diabetes medications such as glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is), which offer cardiorenal protection for people with T2D (PwT2D). This study aimed to characterize the demographic, clinical, and diabetes medication utilization patterns of PwT2D initiating basal insulin between 2014 and 2020 over the time period when these revisions were occurring., Methods: A retrospective study was conducted using the IBM ® MarketScan ® databases and included adults with T2D who initiated basal insulin therapy (basal insulin initiators) in 2015, 2017, or 2019. Patient characteristics, medication utilization patterns, and time to add an additional diabetes drug class were compared across years. Characteristics of users of basal insulin-GLP-1RA combination therapy (GLP-1RA-basal insulin dual users) were also compared across years., Results: Between 2015 and 2019, initiation of basal insulin therapy remained steady, with 1.6-1.9% of PwT2D starting basal insulin in each year. GLP-1RA and SGLT-2i use increased pre- and post-basal insulin initiation (pre-basal: GLP-1RA, from 14.8% to 25.2%, p < 0.0001; SGLT-2i, from 11.4% to 20.5%, p < 0.0001; post-basal: GLP-1RA, from 16.7% to 30.5%, p < 0.0001; SGLT-2i, from 13.4% to 23.3%, p < 0.0001]). The proportion of PwT2D with underlying cardiovascular and renal diseases did not increase during this period. Among basal insulin initiators without prior GLP-1RA, SGLT-2i, or bolus insulin use, time to adding on these agents decreased, with 14.0-15.6% starting bolus insulin within the first year. Among GLP-1RA-basal insulin dual initiators, the proportion of those with underlying cardiovascular disease was not higher among GLP-1RA first users., Conclusions: In this real-world study, insulin remained key in the T2D treatment paradigm. A growing proportion of PwT2D utilized GLP-1RAs and SGLT-2is before and after initiation of basal insulin therapy. At the same time, there was no increase in the proportion of those initiating basal insulin who had cardiorenal comorbidity profiles for which treatment guidelines have recommended the use of GLP-1RAs or SGLT-2is., (© 2023. The Author(s).)

Introduction: Glycated hemoglobin A1c (HbA1c) is an important measure to assess glycemic control and predict diabetes complications. However, there is limited information on trends in HbA1c among people with diabetes (PwDs) who use insulin. The aim of this study was to describe trends in HbA1c among PwDs who use insulin by diabetes type and insulin regimen., Methods: A retrospective analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES, 2009-2020). PwDs were classified into three cohorts: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus using mealtime insulin (T2DM-MTI), and type 2 diabetes mellitus (T2DM) using basal-only insulin (T2DM basal-only). Trends in HbA1c over time were assessed using regression analysis after adjusting for age, gender, and race/ethnicity., Results: Mean HbA1c values aggregated over 2009-2020 were 8.0% (T1DM), 8.6% (T2DM-MTI), and 8.6% (T2DM basal-only). The American Diabetes Association-recommended target of HbA1c of < 7% was achieved by 25.2% of people in the T1DM and T2DM-MTI groups each and by 12.3% of people in the T2DM basal-only group. Over time, an upward trend was observed in the percentage of people achieving HbA1c < 7% in the T2DM basal-only group. The percentage of PwDs achieving individualized HbA1c targets was 27.0%, 12.4%, and 16.1% for the T1DM, T2DM-MTI, and T2DM basal-only groups, respectively., Conclusions: Our study using NHANES data suggests that approximately 25% of PwDs achieve glycemic targets. This study highlights the need for improved therapies to better manage glycemic targets in PwDs., (© 2023. The Author(s).)

As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization., (© 2023. The Author(s).)

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD., Competing Interests: DECLARATION OF INTERESTS The other authors declare no competing financial interests.

Myocardial infarction is a leading cause of death worldwide 1 . Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX + CD8 + T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration., Competing Interests: Declaration of interests B.I. has received honoraria from consulting with Merck, Janssen Pharmaceuticals, Astra Zeneca, and Volastra Therapeutics. M.A.D. has been a consultant to Roche/Genentech, Array, Pfizer (New York, NY, United States of America), Novartis, BMS, GSK, Sanofi-Aventis (Bridgewater, NJ, United States of America), Vaccinex, Apexigen, EISAI, and ABM Therapeutics and he has been the PI of research grants to MD Anderson by Roche/Genentech (South San Francisco, CA, United States of America), GSK, Sanofi-Aventis, Merck, Myriad, and Oncothyreon. A.R. has received honoraria from consulting with CStone, Merck, and Vedanta, is or has been a member of the scientific advisory board and holds stock in Advaxis, Appia, Apricity, Arcus, Compugen, CytomX, Highlight, ImaginAb, ImmPact, ImmuneSensor, Inspirna, Isoplexis, Kite-Gilead, Lutris, MapKure, Merus, PACT, Pluto, RAPT, Synthekine, and Tango, has received research funding from Agilent (Santa Clara, CA, United States of America) and from Bristol-Myers Squibb through Stand Up to Cancer (SU2C), and patent royalties from Arsenal Bio. T.E. has acted as a consultant for Almiral Hermal, Bristol-Myers Squibb, MSD, Novartis, Pierre Fabre, and Sanofi. E.Z.M. is a consultant for Curio Bioscience. The other authors do not declare competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Establishing causal relationships between genetic alterations of human cancers and specific phenotypes of malignancy remains a challenge. We sequentially introduced mutations into healthy human melanocytes in up to five genes spanning six commonly disrupted melanoma pathways, forming nine genetically distinct cellular models of melanoma. We connected mutant melanocyte genotypes to malignant cell expression programs in vitro and in vivo, replicative immortality, malignancy, rapid tumor growth, pigmentation, metastasis, and histopathology. Mutations in malignant cells also affected tumor microenvironment composition and cell states. Our melanoma models shared genotype-associated expression programs with patient melanomas, and a deep learning model showed that these models partially recapitulated genotype-associated histopathological features as well. Thus, a progressive series of genome-edited human cancer models can causally connect genotypes carrying multiple mutations to phenotype.

The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features., (© 2022. The Author(s).)

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software., (© 2021. The Author(s).)

The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis., (© 2022. The Author(s).)

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection 1,2 , but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1β and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1 + pathological fibroblasts 3 contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand-receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development.

Despite initial responses 1-3 , most melanoma patients develop resistance 4 to immune checkpoint blockade (ICB). To understand the evolution of resistance, we studied 37 tumor samples over 9 years from a patient with metastatic melanoma with complete clinical response to ICB followed by delayed recurrence and death. Phylogenetic analysis revealed co-evolution of seven lineages with multiple convergent, but independent resistance-associated alterations. All recurrent tumors emerged from a lineage characterized by loss of chromosome 15q, with post-treatment clones acquiring additional genomic driver events. Deconvolution of bulk RNA sequencing and highly multiplexed immunofluorescence (t-CyCIF) revealed differences in immune composition among different lineages. Imaging revealed a vasculogenic mimicry phenotype in NGFR hi tumor cells with high PD-L1 expression in close proximity to immune cells. Rapid autopsy demonstrated two distinct NGFR spatial patterns with high polarity and proximity to immune cells in subcutaneous tumors versus a diffuse spatial pattern in lung tumors, suggesting different roles of this neural-crest-like program in different tumor microenvironments. Broadly, this study establishes a high-resolution map of the evolutionary dynamics of resistance to ICB, characterizes a de-differentiated neural-crest tumor population in melanoma immunotherapy resistance and describes site-specific differences in tumor-immune interactions via longitudinal analysis of a patient with melanoma with an unusual clinical course.

Targeted inhibition of oncogenic pathways can be highly effective in halting the rapid growth of tumors but often leads to the emergence of slowly dividing persister cells, which constitute a reservoir for the selection of drug-resistant clones. In BRAF V600E melanomas, RAF and MEK inhibitors efficiently block oncogenic signaling, but persister cells emerge. Here, we show that persister cells escape drug-induced cell-cycle arrest via brief, sporadic ERK pulses generated by transmembrane receptors and growth factors operating in an autocrine/paracrine manner. Quantitative proteomics and computational modeling show that ERK pulsing is enabled by rewiring of mitogen-activated protein kinase (MAPK) signaling: from an oncogenic BRAF V600E monomer-driven configuration that is drug sensitive to a receptor-driven configuration that involves Ras-GTP and RAF dimers and is highly resistant to RAF and MEK inhibitors. Altogether, this work shows that pulsatile MAPK activation by factors in the microenvironment generates a persistent population of melanoma cells that rewires MAPK signaling to sustain non-genetic drug resistance., Competing Interests: Declaration of Interests P.K.S. is a member of the SAB or BOD of Glencoe Software, Applied Biomath, and RareCyte and has equity in these companies and is on the SAB of and NanoString. In the last five years the Sorger lab has received research funding from Novartis and Merck. P.K.S. declares that none of these relationships are directly or indirectly related to the content of this manuscript. R.W. is a co-founder of T2Biosystems and Lumicell, serves as a scientific advisor for ModeRNA Therapeutics, Tarveda Therapeutics, and Alivio Therapeutics. None of these activities are related to the manuscript. The other authors declare that they have no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines. Code is available at https://sabdelmagid.github.io/miccai2020-project/.

Background: Mutations in ADCK4 (aarF domain containing kinase 4) generally manifest as steroid-resistant nephrotic syndrome and induce coenzyme Q 10 (CoQ 10 ) deficiency. However, the molecular mechanisms underlying steroid-resistant nephrotic syndrome resulting from ADCK4 mutations are not well understood, largely because the function of ADCK4 remains unknown., Methods: To elucidate the ADCK4's function in podocytes, we generated a podocyte-specific, Adck4 -knockout mouse model and a human podocyte cell line featuring knockout of ADCK4 . These knockout mice and podocytes were then treated with 2,4-dihydroxybenzoic acid (2,4-diHB), a CoQ 10 precursor analogue, or with a vehicle only. We also performed proteomic mass spectrometry analysis to further elucidate ADCK4's function., Results: Absence of Adck4 in mouse podocytes caused FSGS and albuminuria, recapitulating features of nephrotic syndrome caused by ADCK4 mutations. In vitro studies revealed that ADCK4-knockout podocytes had significantly reduced CoQ 10 concentration, respiratory chain activity, and mitochondrial potential, and subsequently displayed an increase in the number of dysmorphic mitochondria. However, treatment of 3-month-old knockout mice or ADCK4-knockout cells with 2,4-diHB prevented the development of renal dysfunction and reversed mitochondrial dysfunction in podocytes. Moreover, ADCK4 interacted with mitochondrial proteins such as COQ5, as well as cytoplasmic proteins such as myosin and heat shock proteins. Thus, ADCK4 knockout decreased the COQ complex level, but overexpression of ADCK4 in ADCK4-knockout podocytes transfected with wild-type ADCK4 rescued the COQ5 level., Conclusions: Our study shows that ADCK4 is required for CoQ 10 biosynthesis and mitochondrial function in podocytes, and suggests that ADCK4 in podocytes stabilizes proteins in complex Q in podocytes. Our study also suggests a potential treatment strategy for nephrotic syndrome resulting from ADCK4 mutations., (Copyright © 2020 by the American Society of Nephrology.)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer., Competing Interests: Declaration of Interests A.R. is a founder of and equity holder in Celsius Therapeutics, an equity holder in Immunitas Therapeutics, and a scientific advisory board (SAB) member of Syros Pharmaceuticals, ThermoFisher Scientific, Asimov, and NeoGene Therapeutics. A.K.S. has received compensation for consulting for and being on the SAB of Honeycomb Biotechnologies, Cellarity, Cogen Therapeutics, and Dahlia Biosciences. O.R.R., A.K.S., and A.R. are co-inventors on patent applications filed by the Broad Institute to inventions relating to single-cell genomics, such as in PCT/US2018/060860 and US provisional application no. 62/745,259. J.W.G. has licensed technologies to Abbott Diagnostics and Danaher and has ownership positions in PDX Pharmaceuticals and Convergent Genomics. He serves as an advisor to New Leaf Ventures and receives private-sector research support from Zeiss, ThermoFisher, Danaher, Micron, PDX Pharmaceuticals, and Quantitative Imaging. C.I.D. receives research support from Bristol-Myers Squibb. S.S. is a consultant for RareCyte, Inc. A.S. is an employee of Johnson & Johnson. S.A.M. has commercial research grants from Johnson & Johnson. P.K.S. is a member of the SAB or board of directors of and has equity in Glencoe Software and RareCyte Inc., which create software and instruments for tissue imaging., (Copyright © 2020. Published by Elsevier Inc.)

Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment. Presence of one or both features associates with an improved outcome while concurrent absence yields no responses. Single-cell spatial analysis reveals prominent interactions of exhausted CD8 + T-cells and PD-L1 + macrophages and PD-L1 + tumor cells as mechanistic determinants of response. Furthermore, spatial analysis of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macrophages in the first, and exhausted CD8 + T-cells with cancer cells harboring genomic PD-L1 and PD-L2 amplification in the second.

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-μm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments., (Copyright © 2018 Elsevier Inc. All rights reserved.)