Cold Spring Harbor Laboratory

Phosphorus Fluoride Exchange (PFEx) is a new click chemistry technology that hinges on the creation of innovative hubs to unlock its potential and broaden its applications. In this study, we outline some guiding principles for PFEx hub development and validate these principles by developing a new hub, 2‐substituted‐alkynyl‐1‐cyclotriphosphazenes (SACPs). These SACPs serve as versatile PFEx hubs with stable functionality and orthogonal reactivity, exemplifying their utility within the Diversity Oriented Clicking (DOC) framework. We demonstrate that SACPs interact with various 1,3‐dipoles, enabling the synthesis of phosphorus‐rich compounds through sequential cycloaddition and PFEx reactions, thus pioneering new synthetic routes for wide application of click chemistry.

Cachexia is a systemic wasting syndrome prevalent in patients with cancer that significantly affects quality of life, health care costs, and therapeutic outcomes. Despite its clinical importance, cachexia is rarely formally diagnosed. This deficiency presents a challenge for effective patient management and care, health care resource allocation, and the advancement of therapeutic approaches. Here, we highlight impedances to the diagnosis and coding of cachexia, including the absence of standardized therapy, a lack of incentives for accurate coding, and overlapping clinical features with other conditions. We differentiate cachexia from related conditions like unintentional weight loss, sarcopenia, frailty, and protein-calorie malnutrition, outlining their distinct clinical features and inter-relations. We propose an approach to enhance diagnostic accuracy and coding for cachexia. This effort will enable better prevalence data, translation of mechanism-based therapy development, patient identification and stratification, and ultimately advanced diagnostics and US Food and Drug Administration–approved treatments for cachexia.

Background Alzheimer’s disease (AD) is a devastating form of dementia, and its prevalence is rising as human lifespan increases. Our lab created the AD‐BXD mouse model, which expresses AD mutations across a genetically diverse reference panel (BXD), to identify factors that confer resilience to cognitive decline in AD. This model mimics key characteristics of human AD including variation in age of onset and severity of cognitive decline. Method To facilitate discovery of conserved mechanisms of resilience to AD, we generated a cross‐species single‐nuclei transcriptomic dataset from normal and AD human (ROSMAP) and AD‐BXD mouse frontal cortex tissue. We interrogated resilience‐associated gene expression signatures, validated resilience candidate genes with human reference data, and used a druggability ranking and drug repositioning pipeline to nominate drugs to promote resilience to AD. To learn more about the context of resilience gene expression, we used a hierarchical mapping algorithm to predict anatomical locations of cells expressing resilience gene signatures. Result We found the strongest gene expression signature associated with cognitive resilience to AD arises from excitatory layer 4/5 (eL4/5) cortical intratelencephalic neurons. This resilience signature includes genes involved in synaptic plasticity, vesicle transport, and axonal and dendritic development. We found that 27 of the 61 genes in the resilience signature are druggable and identified several candidate drugs for further investigation (Telpoukhovskaia et al., 2022). We also identified genes expressed across a continuum of cognitive performance. Our hierarchical mapping approach showed that the eL4/5 neurons expressing resilience signature genes are distributed throughout the frontal cortex, mainly in the somatomotor area. Conclusion We identified 61 candidate resilience genes to target with new or existing drugs. We also determined that expression of resilience candidate genes occurs in eL4/5 neurons in the somatomotor region of the cortex. Ongoing projects in the lab aim to evaluate efficacy of nominated drugs and profile learning‐specific proteomes of eL4/5 neurons in resilient and susceptible AD‐BXD strains. When integrated with existing genetic, behavioral, and pathological data, our work will elucidate the cellular, molecular, and genetic mechanisms that contribute to cognitive resilience in face of neurodegenerative disease pathology.

Genomics is an increasingly important part of biology research. However, educating undergraduates in genomics is not yet a standard part of life sciences curricula. We believe this is, in part, due to a lack of standard concepts for the teaching of genomics. To address this deficit, the members of the Genomics Education Alliance created a set of genomics concepts that was then further refined by input from a community of undergraduate educators who engage in genomics instruction. The final genomics concepts list was compared to existing learning frameworks, including the Vision and Change initiative (V&C), as well as ones for genetics, biochemistry and molecular biology, and bioinformatics. Our results demonstrate that the new genomics framework fills a niche not addressed by previous inventories. This new framework should be useful to educators seeking to design stand-alone courses in genomics as well as those seeking to incorporate genomics into existing coursework. Future work will involve designing curriculum and assessments to go along with this genomics learning framework.

Nuclear speckles are dynamic nuclear bodies characterized by high concentrations of factors involved in RNA production. Although the contents of speckles suggest multifaceted roles in gene regulation, their biological functions are unclear. Here we investigate speckle variation in human cancer, finding two main signatures. One speckle signature was similar to healthy adjacent tissues, whereas the other was dissimilar, and considered an aberrant cancer speckle state. Aberrant speckles show altered positioning within the nucleus, higher levels of the TREX RNA export complex and correlate with poorer patient outcomes in clear cell renal cell carcinoma (ccRCC), a cancer typified by hyperactivation of the HIF-2α transcription factor. We demonstrate that HIF-2α promotes physical association of certain target genes with speckles depending on HIF-2α protein speckle-targeting motifs, defined in this study. We identify homologous speckle-targeting motifs within many transcription factors, suggesting that DNA-speckle targeting may be a general gene regulatory mechanism. Integrating functional, genomic and imaging studies, we show that HIF-2α gene regulatory programs are impacted by speckle state and by abrogation of HIF-2α-driven speckle targeting. These findings suggest that, in ccRCC, a key biological function of nuclear speckles is to modulate expression of select HIF-2α-regulated target genes that, in turn, influence patient outcomes. Beyond ccRCC, tumour speckle states broadly correlate with altered functional pathways and expression of speckle-associated gene neighbourhoods, exposing a general link between nuclear speckles and gene expression dysregulation in human cancer.

Modern maize (Zea mays ssp. mays) was domesticated from Teosinte parviglumis (Zea mays ssp. parviglumis), with subsequent introgressions from Teosinte mexicana (Zea mays ssp. mexicana), yielding increased kernel row number, loss of the hard fruit case and dissociation from the cob upon maturity, as well as fewer tillers. Molecular approaches have identified transcription factors controlling these traits, yet revealed that a complex regulatory network is at play. MaizeCODE deploys ENCODE strategies to catalog regulatory regions in the maize genome, generating histone modification and transcription factor ChIP-seq in parallel with transcriptomics datasets in 5 tissues of 3 inbred lines which span the phenotypic diversity of maize, as well as the teosinte inbred TIL11. Transcriptomic analysis reveals that pollen grains share features with endosperm, and express dozens of “proto-miRNAs” potential vestiges of gene drive and hybrid incompatibility. Integrated analysis with chromatin modifications results in the identification of a comprehensive set of regulatory regions in each tissue of each inbred, and notably of distal enhancers expressing non-coding enhancer RNAs bi-directionally, reminiscent of “super enhancers” in animal genomes. Furthermore, the morphological traits selected during domestication are recapitulated, both in gene expression and within regulatory regions containing enhancer RNAs, while highlighting the conflict between enhancer activity and silencing of the neighboring transposable elements.

Song acquisition behavior observed in the songbird system provides a notable example of learning through trial-and-error which parallels human speech acquisition. Studying songbird vocal learning can offer insights into mechanisms underlying human language. We present a computational model of song learning that integrates reinforcement learning (RL) and Hebbian learning and agrees with known songbird circuitry. The song circuit outputs activity from nucleus RA, which receives two primary inputs: timing information from area HVC and stochastic activity from nucleus LMAN. Additionally, song learning relies on Area X, a basal ganglia area that receives dopaminergic inputs from VTA. In our model, song is first acquired in the HVC-to-Area X connectivity, employing an RL mechanism that involves node perturbation. This information is then consolidated into HVC-to-RA synapses through a Hebbian mechanism. The transfer of weights from Area X to RA takes place via the thalamus, utilizing a specific form of spike-timing-dependent plasticity (STDP). Thus, we present a computational model grounded in songbird circuitry in which the optimal policy is initially guided by RL and subsequently transferred to another circuit through Hebbian plasticity.

The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20μ thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60μ isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.

The olfactory system employs responses of an ensemble of odorant receptors (ORs) to sense molecules and to generate olfactory percepts. Here we hypothesized that ORs can be viewed as 3D spatial filters that extract molecular features relevant to the olfactory system, similarly to the spatio-temporal filters found in other sensory modalities. To build these filters, we trained a convolutional neural network (CNN) to predict human olfactory percepts obtained from several semantic datasets. Our neural network, the DeepNose, produced responses that are approximately invariant to the molecules' orientation, due to its equivariant architecture. Our network offers high-fidelity perceptual predictions for different olfactory datasets. In addition, our approach allows us to identify molecular features that contribute to specific perceptual descriptors. Because the DeepNose network is designed to be aligned with the biological system, our approach predicts distinct perceptual qualities for different stereoisomers. The architecture of the DeepNose relying on the processing of several molecules at the same time permits inferring the perceptual quality of odor mixtures. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts and help identify molecular features responsible for odor quality.

Single-cell or single-nucleus transcriptomics is a powerful tool for identifying cell types and cell states. However, hypotheses derived from these assays, including gene expression information, require validation, and their functional relevance needs to be established. The choice of validation depends on numerous factors. Here, we present types of orthogonal and functional validation experiment to strengthen preliminary findings obtained using single-cell and single-nucleus transcriptomics as well as the challenges and limitations of these approaches.

Human neural organoids offer an exciting opportunity for studying inaccessible human-specific brain development; however, it remains unclear how precisely organoids recapitulate fetal/primary tissue biology. We characterize field-wide replicability and biological fidelity through a meta-analysis of single-cell RNA-sequencing data for first and second trimester human primary brain (2.95 million cells, 51 data sets) and neural organoids (1.59 million cells, 173 data sets). We quantify the degree primary tissue cell type marker expression and co-expression are recapitulated in organoids across 10 different protocol types. By quantifying gene-level preservation of primary tissue co-expression, we show neural organoids lie on a spectrum ranging from virtually no signal to co-expression indistinguishable from primary tissue, demonstrating a high degree of variability in biological fidelity among organoid systems. Our preserved co-expression framework provides cell type-specific measures of fidelity applicable to diverse neural organoids, offering a powerful tool for uncovering unifying axes of variation across heterogeneous neural organoid experiments.

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Cell death plasticity is crucial for modulating tissue homeostasis and immune responses, but our understanding of the molecular components that regulate cell death pathways to determine cell fate remains limited. Here, a CRISPR screen of acute myeloid leukemia cells identifies protein tyrosine phosphatase non-receptor type 23 (PTPN23) as essential for survival. Loss of PTPN23 activates nuclear factor-kappa B, apoptotic, necroptotic, and pyroptotic pathways by causing the accumulation of death receptors and toll-like receptors (TLRs) in endosomes. These effects are recapitulated by depletion of PTPN23 co-dependent genes in the endosomal sorting complex required for transport (ESCRT) pathway. Through proximity-dependent biotin labeling, we show that NAK-associated protein 1 interacts with PTPN23 to facilitate endosomal sorting of tumor necrosis factor receptor 1 (TNFR1), sensitizing cells to TNF-α-induced cytotoxicity. Our findings reveal PTPN23-dependent ESCRT machinery as a cell death checkpoint that regulates the spatiotemporal distribution of death receptors and TLRs to restrain multiple cell death pathways.

We conducted a large-scale whole-brain morphometry study by analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We registered 204 mouse brains of three major imaging modalities to the Allen Common Coordinate Framework (CCF) atlas, annotated 182,497 neuronal cell bodies, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1876 neurons along with their axonal motifs, and detected 2.63 million axonal varicosities that indicate potential synaptic sites. Our analyzed six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, dendritic and axonal arborization, axonal varicosities, and sub-neuronal structural motifs, along with a quantification of the diversity and stereotypy of patterns at each level. This integrative study provides key anatomical descriptions of neurons and their types across a multiple scales and features, contributing a substantial resource for understanding neuronal diversity in mammalian brains.

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression-free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and are associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and the expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.