Biology subjects

Smith, T. S.

Publications and source records attributed to Smith, T. S..

9 recordsLinked to original sources

bioRxiv2026

Robust analysis of comparative subcellular omics with complex designs

Subcellular omics technologies now allow us to obtain insights into steady-state localisation and re-localisation of biomolecules in high-throughput. However, robust analysis of these experiments can be slow and challenging. Here, we show that existing approaches to differential localisation fall into two classes, marker-dependent and marker-free, that fail for fundamentally different statistical reasons, with failure modes that are not completely overlapping. We exploit this observation in SANDLE (statistical analysis of differential localisation experiments), which combines a marker-dependent generative model of subcellular niches with a marker-free regression test for changes in fractionation profiles. This dual strategy provides better control of false positives, up to 100-fold reduction in analysis time, and accommodates more complex experimental designs than existing methods. We demonstrate SANDLEs versatility across a wide range of subcellular omics experiments, including drug-treatment responses, cross-species and life-cycle comparisons, post-translationally modified proteoforms, and RNA re-localisation. Coanalysing transcriptome and proteome dynamics during the unfolded protein response, we further reveal lncRNAs whose steady-state localisation is condition-dependent. By addressing key methodological limitations, SANDLE enables broad applications of subcellular omics from fundamental biology to clinical research.

biochemistry↗

bioRxiv2024

A Stochastic Dynamic Operator framework that improves the precision of analysis and prediction relative to the classical spike-triggered average method, extending the toolkit.

Here we test the Stochastic Dynamic Operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered averaging (STA), or stimulus-triggered averaging, methods currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. SDO methods are more sensitive and specific than the STA for identifying state-dependent relationships in simulated data. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike-signal relationships to be captured, analyzed, and interpreted visually and intuitively. SDO methods can be applied at different scales of interest where spike-triggered averaging methods are currently employed, and beyond, from single neurons to gross motor behaviors. SDOs may be readily generated and analyzed using the provided SDO Analysis Toolkit. We anticipate this method will be broadly useful for describing dynamical signal behavior and uncovering state-dependent relationships of stochastic signals relative to discrete event times. SIGNIFICANCEHere the authors introduce new tools and demonstrate data analysis using a new probabilistic and state-dependent technique, which is an expansion and extension of the classical spike-triggered average, the Stochastic Dynamic Operator. Stochastic Dynamic Operator methods extend application into domains where classical spike triggered averages fail, capture more information on spike correlations, and match or outperform the spike-triggered average when generating predictions of signal amplitude based on spiking events. A data and code package toolkit for utilizing and interpreting Stochastic Dynamic Operator methods is provided together with example analyses. Both the method and the associated toolkit are thus expected to be broadly useful in research domains where the spike triggered average is currently used for analysis, and beyond.

neuroscience↗

bioRxiv2023

Benchmarking tRNA-Seq quantification approaches by realistic tRNA-Seq data simulation identifies two novel approaches with higher accuracy

Quantification of transfer RNA (tRNA) using illumina sequencing based tRNA-Seq is complicated due to their degree of redundancy and extensive modifications. As such, no tRNA-Seq method has become well established, while various approaches have been proposed to quantify tRNAs from sequencing reads. Here, we use realistic tRNA-Seq simulations to benchmark tRNA-Seq quantification approaches, including two novel approaches. We demonstrate that these novel approaches are consistently the most accurate, using data simulated to mimic five different tRNA-Seq methods. This simulation-based benchmarking also identifies specific shortfalls for each quantification approach and suggests that up to 13% of the variance observed between cell lines in real tRNA-Seq data could be due to systematic differences in quantification accuracy.

bioinformatics↗

bioRxiv2023

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous systems actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous systems motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

neuroscience↗

bioRxiv2021

Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

Tandem mass tags (TMT) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software, Tune. However, 53 % of Orbitrap Fusion, Lumos or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Sub-notch intensities are systemically underestimated with Tune version 2, leading to over-estimation of the true differences in intensities between samples. However, when summarising reporter ion intensities to higher level features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.

bioinformatics↗

bioRxiv2019

Efficacy of aerial forward-looking infrared surveys for detecting polar bear maternal dens

Denned polar bears are invisible under the snow, therefore winter-time petroleum exploration and development activities in northern Alaska have potential to disturb maternal polar bears and their cubs. Previous research determined forward looking infrared (FLIR) imagery could detect many polar bear maternal dens under the snow, but also identified limitations of FLIR imagery. We evaluated the efficacy of FLIR-surveys conducted by oil-field operators from 2004-2016. Aerial FLIR surveys detected 15 of 33 (45%) and missed 18 (55%) of the dens known to be within surveyed areas. While greater adherence to previously recommended protocols may improve FLIR detection rates, the physical characteristics of polar bear maternal dens, increasing frequencies of weather unsuitable for FLIR detections--caused by global warming, and competing \"hot spots\" are likely to prevent FLIR surveys from detecting maternal dens reliably enough to afford protections consonant with increasing global threats to polar bear welfare.

zoology↗

bioRxiv2018

Brain-wide cellular resolution imaging of Cre transgenic zebrafish lines for functional circuit-mapping

Decoding the functional connectivity of the nervous system is facilitated by transgenic methods that express a genetically encoded reporter or effector in specific neurons; however, most transgenic lines show broad spatiotemporal and cell-type expression. Increased specificity can be achieved using intersectional genetic methods which restrict reporter expression to cells that co-express multiple drivers, such as Gal4 and Cre. To facilitate intersectional targeting in zebrafish, we have generated more than 50 new Cre lines, and co-registered brain expression images with the Zebrafish Brain Browser, a cellular resolution atlas of 264 transgenic lines. Lines labeling neurons of interest can be identified using a web-browser to perform a 3D spatial search (zbbrowser.com). This resource facilitates the design of intersectional genetic experiments and will advance a wide range of precision circuit-mapping studies.

neuroscience↗

bioRxiv2018

LOPIT-DC: A simpler approach to high-resolution spatial proteomics

Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method for studying protein subcellular localisation in complex biological samples. As a simpler alternative we developed a second workflow named Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) which is faster and less resource-intensive. We present the most comprehensive high-resolution mass spectrometry-based human dataset to date and deliver a flexible set of subcellular proteomics protocols for sample preparation and data analysis. For the first time, we methodically compare these two different mass spectrometry-based spatial proteomics methods within the same study and also apply QSep, the first tool that objectively and robustly quantifies subcellular resolution in spatial proteomics data. Using both approaches we highlight suborganellar resolution and isoform-specific subcellular niches as well as the locations of large protein complexes and proteins involved in signalling pathways which play important roles in cancer and metabolism. Finally, we showcase an extensive analysis of the multilocalising proteome identified via both methods.

biochemistry↗

bioRxiv2018

Alevin: An integrated method for dscRNA-seq quantification

We introduce alevin, a fast end-to-end pipeline to process droplet-based single cell RNA sequencing data, which performs cell barcode detection, read mapping, unique molecular identifier deduplication, gene count estimation, and cell barcode whitelisting. Alevins approach to UMI deduplication accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads, and improves the accuracy of gene abundance estimates.

bioinformatics↗