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[[CV|https://innesbre.github.io/]]<<BR>> | |
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Twitter: [[https://twitter.com/InnesBrendanT|@InnesBrendanT]]<<BR>> ORCID: [[https://orcid.org/0000-0003-2496-3154|0000-0003-2496-3154]]<<BR>> |
Social: [[https://genomic.social/@InnesBT|@InnesBT]]<<BR>> Twitter (deprecated): [[https://twitter.com/InnesBrendanT|@InnesBrendanT]]<<BR>> Github: [[https://github.com/innesbre|innesbre]]<<BR>> Citations at [[https://scholar.google.com/citations?user=zyFRoPQAAAAJ&hl=en|Google Scholar]], [[https://www.semanticscholar.org/author/Brendan-T.-Innes/50131001|Semantic Scholar]], or [[https://orcid.org/0000-0003-2496-3154|ORCID: 0000-0003-2496-3154]]<<BR>> |
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I am a PhD candidate in Molecular Genetics who started in the Bader lab in May, 2016. I am very excited about the possibilities offered by high-throughput single-cell RNA-seq, especially to investigate intercellular signalling in complex tissues. | I am a PhD candidate in Molecular Genetics who started in the Bader lab in May, 2016. I am very excited about the possibilities offered by high-throughput single-cell RNA-seq, especially to investigate intercellular signalling in complex tissues. [[http://shiny.baderlab.org/|Explore the results of some of my work here!]] |
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'''Cell type matters!'''<<BR>> In my recent attempts to improve ligand-receptor interaction prediction from scRNAseq, I've identified a fundamental issue plaguing ''next-gen'' prediction methods that use the receptor cell's transcriptome to help specify which receptors have been activated. [[https://www.biorxiv.org/content/10.1101/2021.09.06.459134v1.full|Transcriptional signatures of cell-cell interactions are dependent on cellular context]]! Methods reliant on prior knowledge of ligand-mediated transcriptional response will not generalize to cell types that they were not trained on, which is a major limitation preventing the field from improving the specificity of our cell-cell interaction predictions. I'm currently working on mitigating this barrier, so stay tuned! |
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MSc in Biochemistry at the University of Western Ontario, supervised by [[https://www.schulich.uwo.ca/biochem/people/bios/Litchfield.html|Dr. David Litchfield]] | MSc in Biochemistry at the University of Western Ontario, supervised by [[https://www.schulich.uwo.ca/biochem/people/faculty/Litchfield.html|Dr. David Litchfield]] |
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== Academic Awards == PRiME Fellowship in Next-Generation Precision Medicine at the University of Toronto (2020) Till & !McCulloch Meeting travel award sponsored by Medicine by Design (2018) David Stephen Cant Graduate Scholarship in Stem Cell Research (2017) |
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TA for MBP1010H - Quantitative Biology and Statistical Methods (2015 - 2017) | TA for MMG1002H - Foundational Genetic Approaches II: Programming for Biologists (2020 - 2021) |
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TA for MMG1002H - Foundational Genetic Approaches II: Programming for Biologists (2020 - 2021) | TA for MBP1010H - Quantitative Biology and Statistical Methods (2015 - 2017) |
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Schwab N, Taskina D, Leung E, '''Innes BT''', Bader GD, Hazrati LN. '''Neurons and glial cells acquire a senescent signature after repeated mild traumatic brain injury in a sex-dependent manner'''. ''Frontiers in Neuroscience'', 2022. https://doi.org/10.3389/fnins.2022.1027116 |
Brendan Innes
https://innesbre.github.io/
Email: <brendan DOT innes AT mail DOT utoronto DOT ca>
Social: @InnesBT
Twitter (deprecated): @InnesBrendanT
Github: innesbre
Citations at Google Scholar, Semantic Scholar, or ORCID: 0000-0003-2496-3154
Looking for a place to discuss all things single-cell?
Join the Toronto scAnalysis Working Group Slack!
I am a PhD candidate in Molecular Genetics who started in the Bader lab in May, 2016. I am very excited about the possibilities offered by high-throughput single-cell RNA-seq, especially to investigate intercellular signalling in complex tissues. Explore the results of some of my work here!
Projects
CCInx - Cell-cell interaction prediction
As part of our ongoing work developing tools to predict cell-cell interaction networks from -omics data, Ruth Isserlin has put together a handy database of ligand-receptor pairs. This database has powered analyses such as this one from the Miller/Kaplan lab, which identified extrinsic regulators of neurogenesis. I'm currently working on CCInx, an R package to generate and visualize bipartite graphs of cell-cell interactions from single-cell RNAseq data. A working development version is available here.
Aging Mouse Brain
Methodios Ximerakis and Scott Lipnick of Lee Rubin's group are studying the effects of aging on the mammalian brain. We are collaborating with them to understand the changes in cell-cell signalling, predicted using CCInx. The results are available online or as an R package.
Cell type matters!
In my recent attempts to improve ligand-receptor interaction prediction from scRNAseq, I've identified a fundamental issue plaguing next-gen prediction methods that use the receptor cell's transcriptome to help specify which receptors have been activated. Transcriptional signatures of cell-cell interactions are dependent on cellular context! Methods reliant on prior knowledge of ligand-mediated transcriptional response will not generalize to cell types that they were not trained on, which is a major limitation preventing the field from improving the specificity of our cell-cell interaction predictions. I'm currently working on mitigating this barrier, so stay tuned!
scClustViz - scRNAseq cluster assessment and visualization
I've built an interactive reporting tool for single-cell RNAseq results called scClustViz (or sCV for short). Hopefully it will both help biologists and bioinformaticians better collaborate while working with this data, and improve open science by making it easier to publish data in an accessible manner.
Human Liver Atlas
Sonya MacParland and Ian McGilvray led the creation of the first single-cell atlas of a human organ, the liver. Jeff Liu and I were honoured to perform the analysis for this project, the results of which can be viewed in scClustViz online or as an R package.
Embryonic Mouse Cerebral Cortex
I worked with Scott Yuzwa and Michael Borrett of Freda Miller's group to understand the precursor population responsible for building the mammalian cerebral cortex, and its relationship with adult neural stem cells. The single-cell transcriptomes from timepoints throughout cortical neurogenesis in the mouse brain can be viewed in scClustViz online or as an R package.
Education
MSc in Biochemistry at the University of Western Ontario, supervised by Dr. David Litchfield
BMSc in Cell Biology and Biochemistry at the University of Western Ontario
Academic Awards
PRiME Fellowship in Next-Generation Precision Medicine at the University of Toronto (2020)
Till & McCulloch Meeting travel award sponsored by Medicine by Design (2018)
David Stephen Cant Graduate Scholarship in Stem Cell Research (2017)
Teaching Experience
TA for MMG1002H - Foundational Genetic Approaches II: Programming for Biologists (2020 - 2021)
TA for MMG1001H - Foundational Genetic Approaches I: Genomics (2019 - 2020)
TA for MBP1010H - Quantitative Biology and Statistical Methods (2015 - 2017)
Publications
Schwab N, Taskina D, Leung E, Innes BT, Bader GD, Hazrati LN. Neurons and glial cells acquire a senescent signature after repeated mild traumatic brain injury in a sex-dependent manner. Frontiers in Neuroscience, 2022. https://doi.org/10.3389/fnins.2022.1027116
Borrett MJ, Innes BT, Tahmasian N, Bader GD, Kaplan DR, Miller FD. A Shared Transcriptional Identity for Forebrain and Dentate Gyrus Neural Stem Cells from Embryogenesis to Adulthood. eNeuro, 2022. https://dx.doi.org/10.1523/ENEURO.0271-21.2021
Coles BLK, Labib M, Poudineh M, Innes BT, Belair-Hickey J, Gomis S, Wang Z, Bader GD, Sargent EH, Kelley SO, van der Kooy D. A microfluidic platform enables comprehensive gene expression profiling of mouse retinal stem cells. Lab on a Chip, 2021. https://doi.org/10.1039/D1LC00790D
Innes BT & Bader GD. Transcriptional signatures of cell-cell interactions are dependent on cellular context. biorxiv, 2021. https://doi.org/10.1101/2021.09.06.459134
Clarke ZA, Andrews TS, Atif J, Pouyabahar D, Innes BT, MacParland SA, Bader GD. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nature Protocols, 2021. https://doi.org/10.1038/s41596-021-00534-0
Borrett MJ, Innes BT, Jeong D, Tahmasian N, Storer MA, Bader GD, Kaplan DR, Miller FD. Single-cell profiling shows murine forebrain neural stem cells reacquire a developmental state when activated for adult neurogenesis. Cell Reports, 2020. https://doi.org/10.1016/j.celrep.2020.108022
Gage B, Lui JC, Innes BT, MacParland SA, McGilvray ID, Bader GD, Keller GM. Generation of functional liver sinusoidal endothelial cells from human pluripotent stem-cell-derived venous angioblasts. Cell Stem Cell, 2020. https://doi.org/10.1016/j.stem.2020.06.007
Ximerakis M, Lipnick SL, Innes BT, Simmons SK, Adiconis X, Dionne D, Nguyen L, Mayweather BA, Ozek C, Niziolek Z, Butty VL, Isserlin R, Buchanan SM, Levine SR, Regev A, Bader GD, Levin JZ, Rubin LL. Single-cell transcriptomic profiling of the aging mouse brain. Nature Neuroscience 2019. https://doi.org/10.1038/s41593-019-0491-3
Innes BT & Bader GD. scClustViz – Single-cell RNAseq cluster assessment and visualization [version 2; peer review: 2 approved]. F1000Research 2019. http://doi.org/10.12688/f1000research.16198.2
MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I, Gupta R, Cheng ML, Liu LY, Camat D, Chung SW, Seliga RK, Shao Z, Lee E, Ogawa S, Ogawa M, Wilson MD, Fish JE, Selzner M, Ghanekar A, Grant D, Greig P, Sapisochin G, Selzner N, Winegarden N, Adeyi O, Keller G, Bader GD, McGilvray ID. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nature Communications 2018. https://doi.org/10.1038/s41467-018-06318-7
Yuzwa SA, Borrett MJ, Innes BT, Voronova A, Ketela T, Kaplan DR, Bader GD, Miller FD. Developmental Emergence of Adult Neural Stem Cells as Revealed by Single-Cell Transcriptional Profiling. Cell Reports 2017. https://doi.org/10.1016/j.celrep.2017.12.017
Innes BT, Sowole MA, Gyenis L, Dubinsky M, Konermann L, Brandl CJ, Litchfield DW, Shilton BH. Peroxide-Mediated Oxidation and Inhibition of the Peptidyl-Prolyl Isomerase Pin1. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 2015. https://doi.org/10.1016/j.bbadis.2014.12.025
Sowole MA, Innes BT, Amunugama M, Brandl CJ, Shilton BH, Litchfield DW, Konermann L. Noncovalent binding of a cyclic peptide inhibitor to the peptidyl-prolyl isomerase Pin1 explored by hydrogen exchange mass spectrometry. Canadian Journal of Chemistry 2014. https://doi.org/10.1139/cjc-2014-0230
Innes BT, Bailey ML, Brandl CJ, Shilton BH, Litchfield DW. Non-catalytic participation of the Pin1 peptidyl-prolyl isomerase domain in target binding. Frontiers in Physiology 2013. https://doi.org/10.3389/fphys.2013.00018