Biology subjects

Macosko, E.

Publications and source records attributed to Macosko, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Cognitive function depends upon Satb2 gene dosage in cortical projection neurons

SATB2-associated syndrome (SAS) is a severe neurodevelopmental disorder caused by de novo heterozygous SATB2 mutations, yet how haploinsufficiency disrupts brain development remains poorly understood. While homozygous Satb2 loss causes profound embryonic cell-fate defects, we demonstrate using a heterozygous mouse model that SAS phenotypes emerge primarily during postnatal circuit maturation. Integrating chromatin profiling, transcriptomics, electrophysiology, and behavior, we show that SATB2 acts as a dose-sensitive chromatin regulator that binds conserved enhancer-promoter landscapes to orchestrate networks linked to human intelligence. Although excitatory neuron subtype specification is preserved, Satb2 heterozygotes adopt an intermediate epigenetic state that drives cell-type-specific dysregulation of genes enriched for intellectual disability risk variants. Consequently, mutant neurons exhibit simplified dendritic arborization, reduced intrinsic excitability, and weakened layer 2/3-to-layer 5 intracortical connectivity. These circuit deficits culminate in the disorganization of the somatosensory barrel cortex and severe impairments in whisker-dependent texture discrimination. Finally, by restricting Satb2 heterozygosity to the cortex, we decouple these cortical sensory deficits from subcortical vocalization phenotypes. Together, our work links SATB2 dosage to chromatin architecture and postnatal circuit maturation, revealing a critical, post-mitotic therapeutic window for intervention in SAS.

neuroscience↗

bioRxiv2026

MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies

Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells, an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimers Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimers disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.

bioinformatics↗

bioRxiv2025

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

cancer biology↗

bioRxiv2024

Cassini: Streamlined and Scalable Method for in situ profiling of RNA and Protein

In the expanding field of spatial genomics, numerous methods have emerged to decode biomolecules in intact tissue. Advanced techniques based on combinatorial decoding can resolve thousands of features in a reasonable time but are often constrained by either the prohibitive costs associated with commercial platforms or the complexity of developing custom instruments. Alternatively, sequential detection methods, like single-molecule FISH, are easier to implement but offer limited multiplexing capability or signal amplification. Here, we introduce Cassini, a new approach for straightforward, cost-effective multiplexed measurements of mRNA and protein features simultaneously. Cassini leverages rolling circle amplification (RCA), known for its robust amplification and remarkable stability even after intense stripping, to serially detect each feature in under 20 minutes of total experimental time. The method also enables simultaneous immunostaining with either fluorophore-conjugated or DNA-barcoded antibodies, through an optimized immunostaining buffer. In a single overnight run, we show that Cassini can quantify 32 features (comprising both RNA and proteins) with sensitivity similar to state-of-the-art FISH techniques. We provide a comprehensive protocol alongside an online probe-design platform (cassini.me), aiming to enhance accessibility and user-friendliness. With our open-source solution, we aspire to empower researchers to uncover the nuances of spatial gene expression dynamics across diverse biological landscapes.

bioengineering↗

bioRxiv2024

Combinatorial expression of neurexin genes regulates glomerular targeting by olfactory sensory neurons

Precise connectivity between specific neurons is essential for the formation of the complex neural circuitry necessary for executing intricate motor behaviors and higher cognitive functions. While trans-interactions between synaptic membrane proteins have emerged as crucial elements in orchestrating the assembly of these neural circuits, the synaptic surface proteins involved in neuronal wiring remain largely unknown. Here, using unbiased single-cell transcriptomic and mouse genetic approaches, we uncover that the neurexin family of genes enables olfactory sensory neuron (OSNs) axons to form appropriate synaptic connections with their mitral and tufted (M/T) cell synaptic partners, within the mammalian olfactory system. Neurexin isoforms are differentially expressed within distinct populations of OSNs, resulting in unique pattern of neurexin expression that is specific to each OSN type, and synergistically cooperate to regulate axonal innervation, guiding OSN axons to their designated glomeruli. This process is facilitated through the interactions of neurexins with their postsynaptic partners, including neuroligins, which have distinct expression patterns in M/T cells. Our findings suggest a novel mechanism underpinning the precise assembly of olfactory neural circuits, driven by the trans-interaction between neurexins and their ligands.

developmental biology↗

bioRxiv2023

Cerebellar circuits for disinhibition and synchronous inhibition

The cerebellar cortex contributes to diverse behaviors by transforming mossy fiber inputs into predictions in the form of Purkinje cell (PC) outputs, and then refining those predictions1. Molecular layer interneurons (MLIs) account for approximately 80% of the inhibitory interneurons in the cerebellar cortex2, and are vital to cerebellar processing1,3. MLIs are thought to primarily inhibit PCs and suppress the plasticity of excitatory synapses onto PCs. MLIs also inhibit, and are electrically coupled to, other MLIs4-7, but the functional significance of these connections is not known1,3. Behavioral studies suggest that cerebellar-dependent learning is gated by disinhibition of PCs, but the source of such disinhibition has not been identified8. Here we find that two recently recognized MLI subtypes2, MLI1 and MLI2, have highly specialized connectivity that allows them to serve very different functional roles. MLI1s primarily inhibit PCs, are electrically coupled to each other, fire synchronously with other MLI1s on the millisecond time scale in vivo, and synchronously pause PC firing. MLI2s are not electrically coupled, they primarily inhibit MLI1s and disinhibit PCs, and are well suited to gating cerebellar-dependent learning8. These findings require a major reevaluation of processing within the cerebellum in which disinhibition, a powerful circuit motif present in the cerebral cortex and elsewhere9-17, greatly increases the computational power and flexibility of the cerebellum. They also suggest that millisecond time scale synchronous firing of electrically-coupled MLI1s helps regulate the output of the cerebellar cortex by synchronously pausing PC firing, which has been shown to evoke precisely-timed firing in PC targets18.

neuroscience↗

bioRxiv2023

Thyroid hormone rewires cortical circuits to coordinate body-wide metabolism and exploratory drive

Animals adapt to varying environmental conditions by modifying the function of their internal organs, including the brain. To be adaptive, alterations in behavior must be coordinated with the functional state of organs throughout the body. Here we find that thyroid hormone-- a prominent regulator of metabolism in many peripheral organs-- activates cell-type specific transcriptional programs in anterior regions of cortex of adult mice via direct activation of thyroid hormone receptors. These programs are enriched for axon-guidance genes in glutamatergic projection neurons, synaptic regulators across both astrocytes and neurons, and pro-myelination factors in oligodendrocytes, suggesting widespread remodeling of cortical circuits. Indeed, whole-cell electrophysiology recordings revealed that thyroid hormone induces local transcriptional programs that rewire cortical neural circuits via pre-synaptic mechanisms, resulting in increased excitatory drive with a concomitant sensitization of recruited inhibition. We find that thyroid hormone bidirectionally regulates innate exploratory behaviors and that the transcriptionally mediated circuit changes in anterior cortex causally promote exploratory decision-making. Thus, thyroid hormone acts directly on adult cerebral cortex to coordinate exploratory behaviors with whole-body metabolic state.

neuroscience↗

bioRxiv2023

The cell type composition of the adult mouse brain revealed by single cell and spatial genomics

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-seq with Slide-seq-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 signaling, 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 (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.

neuroscience↗

bioRxiv2023

HiDDEN: A machine learning label refinement method for detection of disease-relevant populations in case-control single-cell transcriptomics

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, but only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDENs superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in previously considered healthy early stage samples. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find a wide usage in contexts which require the detection of subtle changes in cell types across conditions.

bioinformatics↗

bioRxiv2022

Brain region-specific changes in neurons and glia and dysregulation of dopamine signaling in Grin2a mutant mice

Schizophrenia disease mechanisms remain poorly understood, in large part due to a lack of valid animal models. Rare heterozygous loss-of-function mutations in GRIN2A, encoding a subunit of the NMDA (N-methyl-d-aspartate) receptor, greatly increase the risk of schizophrenia. By transcriptomic, proteomic, electroencephalogram (EEG) recording and behavioral analysis, we report that heterozygous Grin2a mutant mice show: (i) large-scale gene expression changes across multiple brain regions and in neuronal (excitatory and inhibitory) and non-neuronal cells (astrocytes, oligodendrocytes); (ii) evidence of reduced activity in prefrontal cortex and increased activity in hippocampus and striatum; (iii) elevated dopamine signaling in striatum; (iv) altered cholesterol biosynthesis in astrocytes; (v) reduction of glutamatergic receptor signalin g proteins in the synapse; (iv) heightened gamma oscillation power in EEG; (vi) aberrant locomotor behavioral pattern opposite of that induced by antipsychotic drugs. These findings reveal potential pathophysiologic mechanisms, provide support for both the "hypo-glutamate" and "hyper-dopamine" hypotheses of schizophrenia, and underscore the utility of Grin2a-deficient mice as a new genetic model of schizophrenia.

neuroscience↗

bioRxiv2022

Cortical somatostatin interneuron subtypes form cell-type specific circuits

The cardinal interneuron classes are a useful simplification of cortical interneuron diversity, but such broad subgroupings glosses over the molecular, morphological, and circuit specificity of interneuron subtypes, most notably among the somatostatin interneuron class. The organizing principles by which the connectivity of these subtypes is specified are unknown. To address this knowledge gap, we designed a series of genetic strategies to target the breadth of somatostatin interneuron subtypes. Using these strategies to target three subtypes that span the entire cortical column, we examined their afferent and efferent connectivity. Our data demonstrated that each of these possesses remarkable reciprocal connectivity with the intracortical or corticofugal pyramidal classes, as well as parvalbumin interneurons. Even when two interneuron subtypes shared the same efferent target, their synaptic targeting proved selective for particular dendritic compartments. We thus provide evidence that subtypes of somatostatin cortical interneurons form cell-type specific cortical circuits.

neuroscience↗

bioRxiv2022

A resource for generating and manipulating human microglial states in vitro

Microglia have emerged as key players in the pathogenesis of neurodegenerative conditions such as Alzheimers disease (AD). In response to CNS stimuli, these cells adopt distinct transcriptional and functional subtypes known as states. However, an understanding of the function of these states has been elusive, especially in human microglia, due to lack of tools to model and manipulate this cell-type. Here, we provide a platform for modeling human microglia transcriptional states in vitro. Using single-cell RNA sequencing, we found that exposure of human stem-cell differentiated microglia (iMGLs) to brain-related challenges generated extensive transcriptional diversity which mapped to gene signatures identified in human brain microglia. We identified two in vitro transcriptional clusters that were analogous to human and mouse disease-associated microglia (DAMs), a state enriched in neurodegenerative disease contexts. To facilitate scalable functional analyses, we established a lentiviral approach enabling broad and highly efficient genetic transduction of microglia in vitro. Using this new technology, we demonstrated that MITF (Melanocyte Inducing Transcription Factor), an AD-enriched transcription factor in microglia, drives both a disease-associated transcriptional signature and a highly phagocytic state. Finally, we confirmed these results across iMGLs differentiated from multiple iPSC lines demonstrating the broad utility of this platform. Together, these tools provide a comprehensive resource that enables the manipulation and functional interrogation of human microglial states in both homeostatic and disease-relevant contexts.

neuroscience↗

bioRxiv2021

Cell type-specific differential expression for spatial transcriptomics

Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show that current approaches cannot learn cell type-specific DE due to changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type. Here, we introduce a statistical method, Cell type-Specific Inference of Differential Expression (C-SIDE), that identifies cell type-specific patterns of differential gene expression while accounting for localization of other cell types. We model spatial transcriptomics gene expression as an additive mixture across cell types of general log-linear cell type-specific expression functions. This approach provides a unified framework for defining and identifying gene expression changes in a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MER-FISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimers mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our C-SIDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr.

genomics↗

bioRxiv2021

A molecular census of midbrain dopaminergic neurons in Parkinson's disease

Midbrain dopamine (DA) neurons in the substantia nigra pars compacta (SNpc) project widely throughout the central nervous system, playing critical roles in voluntary movements, reward processing, and working memory. Many of these neurons are highly sensitive to neurodegeneration in Parkinsons Disease (PD), and their loss correlates strongly with the pathognomonic symptoms. To characterize these populations molecularly, we developed a protocol to enrich and transcriptionally profile DA neuron nuclei from postmortem human SNpc of both PD patients and matched controls. We identified a total of ten distinct populations, including one that was primate-specific. A single subtype, marked by the gene AGTR1, was highly susceptible to degeneration, and was enriched for expression of genes associated with PD in genetic studies, suggesting many risk loci act within this subtype to influence its neurodegeneration. The AGTR1 subtype also showed the strongest upregulation of TP53 and its downstream targets, nominating a potential pathway of degeneration in vivo. The transcriptional characterization of differentially disease-vulnerable DA neurons in the SNpc will inform the development of laboratory models, enable the nomination of novel disease biomarkers, and guide further studies of pathogenic disease mechanisms.

neuroscience↗

bioRxiv2020

Graded heterogeneity of metabotropic signaling underlies a continuum of cell-intrinsic temporal responses

Many neuron types consist of populations with continuously varying molecular properties. Here we show a continuum of postsynaptic molecular properties in three types of neurons and assess the functional consequences in cerebellar unipolar brush cells (UBCs). While UBCs are thought to form discrete functional subtypes, with mossy fiber (MF) activation increasing firing in ON-UBC and suppressing firing in OFF-UBC. We find instead a continuum of response profiles that reflect the graded and inversely correlated expression of excitatory mGluR1 and inhibitory mGluR2/3 pathways. MFs coactivate mGluR2/3 and mGluR1 in many UBCs, leading to sequential inhibition-excitation because mGluR2/3-currents are faster. Additionally, we show that DAG kinase controls mGluR1 response duration, and that graded DAG kinase levels account for systematic variation of response duration over two orders of magnitude. These results demonstrate that continuous variations in metabotropic signaling can generate a stable cell-autonomous basis for temporal integration and learning over multiple time scales.

neuroscience↗

bioRxiv2020

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organs common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7-10. Conversely, Spatial Transcriptomics allows for in-situ measurements11-13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1-3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with [~]30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.

genomics↗

bioRxiv2020

A transcriptomic atlas of the mouse cerebellum reveals regional specializations and novel cell types

The cerebellum is a well-studied brain structure with diverse roles in motor learning, coordination, cognition, and autonomic regulation. Nonetheless, a complete inventory of cerebellar cell types is presently lacking. We used high-throughput transcriptional profiling to molecularly define cell types across individual lobules of the adult mouse cerebellum. Purkinje and granule neurons showed considerable regional specialization, with the greatest diversity occurring in the posterior lobules. For multiple types of cerebellar interneurons, the molecular variation within each type was more continuous, rather than discrete. For the unipolar brush cells (UBCs)--an interneuron population previously subdivided into two discrete populations--the continuous variation in gene expression was associated with a graded continuum of electrophysiological properties. Most surprisingly, we found that molecular layer interneurons (MLIs) were composed of two molecularly and functionally distinct types. Both show a continuum of morphological variation through the thickness of the molecular layer, but electrophysiological recordings revealed marked differences between the two types in spontaneous firing, excitability, and electrical coupling. Together, these findings provide the first comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological, and physiological ontologies for defining brain cell types.

genomics↗

bioRxiv2018

Integrative inference of brain cell similarities and differences from single-cell genomics

Defining cell types requires integrating diverse measurements from multiple experiments and biological contexts. Recent technological developments in single-cell analysis have enabled high-throughput profiling of gene expression, epigenetic regulation, and spatial relationships amongst cells in complex tissues, but computational approaches that deliver a sensitive and specific joint analysis of these datasets are lacking. We developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity, allowing flexible modeling of highly heterogeneous single-cell datasets. We demonstrated its broad utility by applying it to four diverse and challenging analyses of human and mouse brain cells. First, we defined both cell-type-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis, an anatomically complex brain region that plays important roles in sex-specific behaviors. Second, we analyzed gene expression in the substantia nigra of seven postmortem human subjects, comparing cell states in specific donors, and relating cell types to those in the mouse. Third, we jointly leveraged in situ gene expression and scRNA-seq data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical scRNA-seq profiles with single-cell DNA methylation signatures, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using the LIGER algorithm promise to accelerate single-cell investigations of cell-type definition, gene regulation, and disease states.

neuroscience↗