Biology subjects

Loper, J.

Publications and source records attributed to Loper, J..

7 recordsLinked to original sources

bioRxiv2025

Enhancer Dynamics and Spatial Organization Drive Anatomically Restricted Cellular States in the Human Spinal Cord

Here, we report the spatial organization of RNA transcription and associated enhancer dynamics in the human spinal cord at single-cell and single-molecule resolution. We expand traditional multiomic measurements to reveal epigenetically poised and bivalent active transcriptional enhancer states that define cell type specification. Simultaneous detection of chromatin accessibility and histone modifications in spinal cord nuclei reveals previously unobserved cell-type specific cryptic enhancer activity, in which transcriptional activation is uncoupled from chromatin accessibility. Such cryptic enhancers define both stable cell type identity and transitions between cells undergoing differentiation. We also define glial cell gene regulatory networks that reorganize along the rostrocaudal axis, revealing anatomical differences in gene regulation. Finally, we identify the spatial organization of cells into distinct cellular organizations and address the functional significance of this observation in the context of paracrine signaling. We conclude that cellular diversity is best captured through the lens of enhancer state and intercellular interactions that drive transitions in cellular state. This study provides fundamental insights into the cellular organization of the healthy human spinal cord.

neuroscience↗

bioRxiv2024

Distinct anatomical and functional corticospinal inputs innervate different spinal neuron types

The corticospinal tract exerts its influence on movement through spinal neurons, which can be divided into types that exhibit distinct functions. However, it remains unknown whether these functional distinctions are reflected in the corticospinal inputs that different types of spinal neurons receive. Using rabies monosynaptic tracing from individual neuron types in the cervical cord and 3D histological reconstruction in mice, we discovered that different types receive inputs distinctly distributed across cortex, and aligned with cell type function. This included a distinct, sparse distribution of direct inputs from cortex onto motor neurons. Coupling rabies tracing with activity measurement during motor behavior revealed different interneuron types receive different input activity patterns, primarily due to the topographical distribution of the corticospinal neurons contacting them. Our results establish that different spinal neuron types get distinct anatomical and functional inputs from the cortex, and reveal functionally relevant homology to primate corticospinal organization.

neuroscience↗

bioRxiv2021

Improved blind demixing methods for recovering dense neuronal morphology from barcode imaging data

Cellular barcoding methods offer the exciting possibility of infinite-pseudocolor anatomical reconstruction -- i.e., assigning each neuron its own random unique barcoded pseudocolor, and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron. We also develop a neural network which uses these barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, connecting the dots between discontiguous amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.

neuroscience↗

bioRxiv2021

A polyyne toxin produced by an antagonistic bacterium blinds and lyses a green microalga

Microalgae are key contributors to global carbon fixation and the basis of many food webs. In nature, their growth is often supported or suppressed by other microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology. Using a combination of Raman microspectroscopy, genome mining and mutational analysis, we discovered a novel polyyne toxin we name protegencin that is secreted by P. protegens and penetrates algal cells to destroy their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin prevents the phototactic behavior of C. reinhardtii needed to perform optimal photosynthesis. A protegencin-deficient biosynthetic mutant of P. protegens does not affect growth or eyespot carotenoids of C. reinhardtii. Thus, protegencin acts in a direct and destructive way, and reveals at least a two-pronged molecular strategy used by algicidal bacteria.

plant biology↗

bioRxiv2020

BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply Bar-Densr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the NeuroCAAS cloud platform. Author SummarySpatial transcriptomics technologies allow us to simultaneously detect multiple molecular targets in the context of intact tissues. These experiments yield images that answer two questions: which kinds of molecules are present, and where are they located in the tissue? In many experiments (e.g., mapping RNA expression in fine neuronal processes), it is desirable to increase the signal density relative to the imaging resolution. This may lead to mixing of signals from multiple RNA molecules into single imaging voxels; thus we need to demix the signals from these images. Here we introduce BarDensr, a new computational method to perform this demixing. The method is based on a forward model of the imaging process, followed by a convex optimization approach to approximately invert mixing induced during imaging. This new approach leads to significantly improved performance in demixing imaging data with dense expression and/or low spatial resolution.

genomics↗

bioRxiv2018

The Markov link method: a nonparametric approach to combine observations from multiple experiments

This paper studies measurement linkage. An example from cell biology helps explain the problem: imagine for a given cell we can either sequence the cells RNA or we can examine its morphology, but not both. Given a cells morphology, what do we expect to see in its RNA? Given a cells RNA, what do we expect in its morphology? More broadly, given a measurement of one type, can we predict measurements of the other type? This measurement linkage problem arises in many scientific and technological fields. To solve this problem, we develop a nonparametric approach we dub the \"Markov link method\" (MLM). The MLM makes a conditional independence assumption that holds in many multi-measurement contexts and provides a way to estimate the link, the conditional probability of one type of measurement given the other. We derive conditions under which the MLM estimator is consistent and we use simulated data to show that it provides accurate measures of uncertainty. We evaluate the MLM on real data generated by a pair of single-cell RNA sequencing techniques. The MLM characterizes the link between them and helps connect the two notions of cell type derived from each technique. Further, the MLM reveals that some aspects of the link cannot be determined from the available data, and suggests new experiments that would allow for better estimates.\n\nSignificance StatementNovel experimental techniques are developing quickly, and each technique gives new perspectives. Ideally we would build theories that account for many perspectives at once. This is not easy. One challenge is that many experiments use measurement techniques that alter or destroy the subject, making it impossible to measure the same subject with both techniques and difficult to combine data from different experiments. In this paper we develop the Markov Link Method, a new tool that overcomes this challenge.

bioinformatics↗

bioRxiv2018

Base-pair Ambiguity and the Kinetics of RNA Folding: a Hypothesis-Driven Statistical Analysis

BackgroundA folding RNA molecule encounters multiple opportunities to form non-native yet energetically favorable pairings of nucleotide sequences. Given this forbidding free-energy landscape, mechanisms have evolved that contribute to a directed and efficient folding process, including catalytic proteins and error-detecting chaperones. Among structural RNA molecules we make a distinction between \"bound\" molecules, which are active as part of ribonucleoprotein (RNP) complexes, and \"unbound,\" with physiological functions performed without necessarily being bound in RNP complexes. We hypothesized that unbound molecules, lacking the partnering structure of a protein, would be more vulnerable than bound molecules to kinetic traps that compete with native stem structures. We defined an \"ambiguity index\"--a normalized function of the primary and secondary structure of an individual molecule that measures the number of kinetic traps available to nucleotide sequences that are paired in the native structure, presuming that unbound molecules would have lower indexes. The ambiguity index depends on the purported secondary structure, and was computed under both the comparative (\"gold standard\") and an equilibrium-based prediction which approximates the minimum free energy (MFE) structure. Arguing that kinetically accessible metastable structures might be more biologically relevant than thermodynamic equilibrium structures, we also hypothesized that MFE-derived ambiguities would be less effective in separating bound and unbound molecules.\n\nResultsWe have introduced an intuitive and easily computed function of primary and secondary structures that measures the availability of complementary sequences that could disrupt the formation of native stems on a given molecule--an ambiguity index. Using comparative secondary structures, the ambiguity index is systematically smaller among unbound than bound molecules, as expected. Furthermore, the effect is lost when the presumably more accurate comparative structure is replaced instead by the MFE structure.\n\nConclusionsA statistical analysis of the relationship between the primary and secondary structures of non-coding RNA molecules suggests that stem-disrupting kinetic traps are substantially less prevalent in molecules not participating in RNP complexes. In that this distinction is apparent under the comparative but not the MFE secondary structure, the results highlight a possible deficiency in structure predictions when based upon assumptions of thermodynamic equilibrium.

bioinformatics↗