Biology subjects

Poldsam, H.

Publications and source records attributed to Poldsam, H..

5 recordsLinked to original sources

bioRxiv2026

Deep Cellular and Spatial Profiling of the Mouse Spinal Cord Reveals Sex-Specific Neuron Types and the Ascending Projection Neuron Repertoire

The spinal cord gives rise to central somatosensation and orchestrates autonomic and motor control. How its cellular diversity achieves functions across sex, longitudinal axis and links the spinal cord to the brain remains poorly understood. Here we used retrograde viral tracing with spatial transcriptomics and multiomic profiling of over 750,000 mouse spinal cord neurons to define its output logic and functional cellular repertoire. We identified 78 ascending spinal projection neuron classes that link it to hind-, mid-, and forebrain centers for itch, touch, and pain revealing its output wiring logic. Furthermore, we characterized >500 anatomically and transcriptomically distinct neuron types with extensive ([~]55%) rostro-caudal specialization as well as first sex specific spinal neurons. Finally, we assembled a cell-type-to-phenotype map of spinal output neurons based on genetically defined interventions. Consequently, we deliver an anatomically and functionally annotated atlas of the adult mouse spinal cord to guide future interrogation of the organ.

neuroscience↗

bioRxiv2026

A consensus spinal cord cell type atlas across mouse, macaque, and human

The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which are implicated in diverse neurological diseases and injuries. Yet the field lacks a comprehensive molecular characterization of cellular diversity in human, macaque, and mouse spinal cord. Here, we present a unified, cross-species cell type atlas based on the integration of single-nucleus gene expression, chromatin accessibility, and spatial transcriptomic data from segments within cervical, thoracic, lumbar, and sacral regions, including motor neurons (MNs) sampled across the entire rostro-caudal axis of the macaque spinal cord. Leveraging the spatial distributions of our molecularly defined cell types, we generated a cell type-guided anatomical map of spinal cord laminae and nuclei. We identified both conserved and species-specific cellular features, including gene expression patterns across distinct MN subtypes in the primate spinal cord. Cross-species cis-regulatory analysis and deep learning sequence models dissected the enhancer logic underlying viral targeting, uncovering conserved transcription factor grammar encoding cellular identity. Together, these results establish a unifying molecular and anatomical taxonomy of spinal cord cell types across species.

neuroscience↗

bioRxiv2025

A molecular map of the human spinal dorsal and ventral horn defines arrangement of neuronal types and glial sex differences

The spinal cord is the gateway for somatosensory and nociceptive information to the brain and a key locus for sensory-motor integration. Studies in mice have advanced our understanding of spinal cord circuitry, and transcriptomic studies have begun to characterize the human spinal cord; however, major gaps in knowledge persist. We conducted single-nucleus sequencing of lumbar spinal cord tissue from 11 adult organ donors and annotated spinal cord cell types with high resolution spatial transcriptomics. We identified 34 spatially and transcriptionally defined neuronal classes and detected sex-specific cell types and states across multiple glial populations. Electrophysiological recordings from dorsal horn neurons revealed firing patterns for neuronal subtypes and group I mGluR-dependent plasticity. Our work defines previously unknown aspects of human spinal cord molecular anatomy and physiology.

neuroscience↗

bioRxiv2025

Unbiased Quantification of Persistent Postural and Motor Deficits Following Spinal Cord Injury in Mice

Spinal cord injury (SCI) causes multifaceted postural and motor impairments that are challenging to quantify. Conventional behavioral tests, such as the Basso mouse scale (BMS), rely on qualitative observations, which do not detect subtle yet significant functional deficits. To perform unbiased and quantitative evaluation of motor function and posture after SCI, we employed high-speed video tracking with machine learning-based whole body pose estimation and weight-bearing analyses using the Blackbox system in freely moving mice. We found enduring alterations in posture induced by SCI not captured by conventional metrics. Postural deficits included reduced hindpaw spacing with concomitant increased forepaw spacing, narrowed femur width, and altered hind paw angles, which remained evident beyond 42 days post-injury (dpi). Furthermore, sustained deficits in locomotor activity were identified as decreased distance traveled, decreased exploratory behavior, and disrupted fore-to-hindpaw speed ratio. Additionally, we analyzed behavioral motifs using Keypoint MoSeq software and found frequency changes in unique motor syllables correlating with forward acceleration and turning after SCI. Interestingly, while motor deficits persisted, sensory deficits, such as thermal and mechanical sensitivity, returned to baseline levels by 21 days after injury in C57Bl/6J mice, with no subsequent hypersensitivity. Lastly, we developed a web-based application to assist in visualization and analyses of Blackbox-based kinematic data. Altogether, our study identifies distinct postural and motor deficits pre- and post-SCI using accurate, unbiased, and quantitative behavioral assessments. By tracking the unique features of motor recovery trajectories, researchers can more accurately assess the effectiveness of SCI therapeutic strategies.

neuroscience↗

bioRxiv2022

Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data

Droplet-based 3 single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3 scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3 to known gene boundaries due to poor 3 UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3 gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.

genomics↗