Biology subjects

Maher, K.

Publications and source records attributed to Maher, K..

5 recordsLinked to original sources

bioRxiv2024

Harmonic representations of regions and interactions in spatial transcriptomics

Spatial transcriptomics technologies enable unbiased measurement of the cell-cell interactions underlying tissue structure and function. However, most unsupervised methods instead focus on identifying tissue regions, representing them as positively covarying low-frequency spatial patterns of gene expression over the tissue. Here, we extend this frequency-based (i.e. harmonic) approach to show that negatively covarying high frequencies represent interactions. Similarly, combinations of low and high frequencies represent interactions along large length scales, or, equivalently, region boundaries. The resulting equations further reveal a duality in which regions and interactions are complementary representations of the same underlying information, each with unique strengths and weaknesses. We demonstrate these concepts in multiple datasets from human lymph node, human tonsil, and mouse models of Alzheimers disease. Altogether, this work offers a conceptually consistent quantitative framework for spatial transcriptomics.

bioinformatics↗

bioRxiv2023

Environmental DNA is more effective than soil-pit hand sorting in evaluating earthworm biodiversity responses to more regenerative agricultural management

Regenerating soil biodiversity is vital to help reverse declines in the health of agricultural soils caused by intensification, and to support sustainable food production and agro-ecosystem services. Earthworms are key functional components of soil biodiversity, with different ecotypes and species delivering specific beneficial soil functions. However, conventional monitoring by hand-sorting from soil pits is highly labour intensive, can reliably identify only adults to species, and may under-record anecics (deep-burrowing ecotypes). Here, we compare soil environmental DNA (eDNA) metabarcoding using two different primer sets and next-generation sequencing, with hand-sorting from standard soil-pits. The experiment comprised four conventionally managed arable fields into which strips of grass-clover ley had been introduced three years earlier. Earthworm population responses had been recorded by hand-sorting for the first two years and our goal was to assess these in the third year and to compare them both by hand and with the use of eDNA. The eDNA method found the same 8 species as hand-sorting, but had greater power for detecting anecic earthworms and quantifying local species richness. Earthworm abundance increased by over 55% into the third year of the leys, surpassing abundances in adjacent permanent grasslands, helping to explain the observed soil health regeneration. Both overall relative abundances and site occupancy proportions of earthworm eDNA were found to have potential as proxies for abundance, and the performance of each of these measures and the implications for further work are discussed. We demonstrate that eDNA can overcome some of the significant barriers and limitations to monitoring earthworm diversity and recommend its wider use both to better understand the earthworm population benefits of different soil management practices, and to guide agricultural policy and practice decisions affecting soil health.

ecology↗

bioRxiv2023

Mitigating autocorrelation during spatially resolved transcriptomics data analysis

Several computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns. We find that randomly sub-sampling neighbors before smoothing mitigates autocorrelation, improving the performance of existing methods and further enabling a simpler, more efficient approach that we call spatial integration (SPIN). SPIN leverages the conventional single-cell toolkit, yielding spatial analogies to each tool: clustering identifies molecular tissue regions; differentially expressed gene analysis calculates region marker genes; trajectory inference reveals continuous, molecularly defined ana tomical axes; and integration allows joint analysis across multiple SRT datasets, regardless of tissue morphology, spatial resolution, or experimental technology. We apply SPIN to SRT datasets from mouse and marmoset brains to calculate shared and species-specific region marker genes as well as a molecularly defined neocortical depth axis along which several genes and cell types differ across species.

bioinformatics↗

bioRxiv2022

Spatiotemporally resolved transcriptomics reveals subcellular RNA kinetic landscape

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function1. However, the intricate subcellular dynamics of RNA synthesis, decay, export, and translocation remain obscured due to the limitations of existing transcriptomics methods2-8. Here, we report a spatiotemporally resolved RNA mapping method (TEMPOmap) to uncover subcellular RNA profiles across time and space at the single-cell level in heterogeneous cell populations. TEMPOmap integrates pulse-chase metabolic labeling of the transcriptome with highly multiplexed three-dimensional (3D) in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape of 991 genes in human HeLa cells from upstream transcription to downstream subcellular translocation. Clustering analysis of critical RNA kinetic parameters across single cells revealed kinetic gene clusters whose expression patterns were shaped by multi-step kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated to serve molecular and cellular functions in cell-cycle dependent manner. Together, these single-cell spatiotemporally resolved transcriptomics measurements provide us the gateway to uncover new gene regulation principles and understand how kinetic strategies enable precise RNA expression in time and space.

genomics↗

bioRxiv2018

A circuit motif in the songbird basal ganglia for computing predicted performance quality

Ventral tegmental area (VTA) dopamine neurons signal prediction error, the difference between actual and predicted outcome, but it remains unclear how error is computed. Here we identify in songbirds a ventral basal ganglia (vBG) region that is required for song learning and that sends prediction error signals to VTA. During singing, vBG neurons heterogeneously encoded song timing, auditory error, predicted error, and the difference between the two (prediction error). Viral tracing revealed inputs to vBG from auditory and vocal motor thalamus, auditory and vocal motor cortex, and VTA. Our findings reveal a classic actor-critic circuit motif in which a ventral critic learns the prediction component of a prediction error signal that is relayed by VTA to a dorsal actor (the vocal motor BG nucleus Area X). A circuit motif for computing reward prediction error can compute predicted performance quality during motor sequence learning.

neuroscience↗