February ABC Seminar: Vitalii Kleshchevnikov, PhD – Wellcome Sanger Institute – “Probabilistic models to resolve cell identity and tissue architecture”

February ABC Seminar: Vitalii Kleshchevnikov, PhD – Wellcome Sanger Institute – “Probabilistic models to resolve cell identity and tissue architecture”

Cell identity drives cell-cell communication and tissue architecture and is in return regulated by cell-extrinsic cues. Cell identity is determined by the combination of intrinsic developmentally established transcription factor use (TF) and constitutive as well as cell communication-dependent TF activities. Presented work shows two probabilistic models that we developed to advance the understanding of these processes using single-cell and spatial genomic data.

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present cell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assess cell2location in three different tissues and demonstrate improved mapping of fine-grained cell types. In the mouse brain, we discover fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially map a rare pre-germinal center B cell population. In the human gut, we resolve fine immune cell populations in lymphoid follicles. Collectively our results present cell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.

Python package is provided here:  https://github.com/BayraktarLab/cell2location.

Cell identity and plasticity is regulated by a combinatorial code mediated by transcription factors and the cell communication environment. Systematically dissecting how the regulatory code robustly defines the vast complexity of cell populations across tissues is a long-standing challenge. Measured using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), DNA accessibility provides a readout of intermediate gene regulation steps at single-cell resolution, with technologies measuring both RNA and ATAC providing the necessary evidence to build mechanistic models of regulation. Existing methods address one or several subproblems of modelling DNA accessibility. For example, the DNA sequence-based deep learning models represent combinatorial interactions and in-vivo TF-DNA recognition preferences. In contrast, GRN models use TF abundance profiles across cells and in-vitro-derived TF-DNA recognition preferences, optionally incorporating ATAC-seq data as a filter. All models learn cell-type specific weights and properties and don’t generalize to new TF abundance states such as new cell types. Therefore, we are missing an end-to-end mechanistic model that represents all steps of the biological process, that generalizes to both new DNA sequences and TF abundance combinations and can simultaneously characterize hundreds to thousands of cell states observed in single-cell genomics atlases. Here, we formulated cell2state, a mechanistic end-to-end probabilistic model of TF recruitment to a chromatin locus and downstream TF effect on DNA accessibility. Cell2state is designed to achieve the generalization of regulatory predictions to unseen cell types. Cell2state A) estimates TF nuclear protein abundance and models B) how TFs recognize DNA, C) how TF sites in DNA lead to TF recruitment to a chromatin locus, D) how the activity of DNA-associated TFs affects chromatin accessibility. To evaluate generalization, we defined the computational problem and developed a workflow for predicting the scATAC-seq readout for previously unseen chromosomes and cell types. We show that cell2state outperforms the state-of-the-art deep learning models (ChromDragoNN) at explaining DNA accessibility differences across cells. Finally, to look at cell state plasticity, we developed ways to use cell2state to simulate the possible chromatin states given TF abundance of source cell types.

Speaker:  Vitalii Kleshchevnikov, PhD
Affiliation:  Wellcome Sanger Institute
Position:  Bioinformatician @ Bayraktar, Stegle, Teichmann group
Host: Daniel MacDonald, Gibson Lab

Date: Monday February 26, 2024
Time: 10:00AM-11:00AM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Vitalii Kleshchevnikov is driven by a deep interest in three key areas: i) understanding the regulatory code which allows a single genome to specify the full diversity of cell populations and their interaction, ii) formalizing the biology of these processes into mechanistic AI/ML models, and iii) accelerating the therapy development to address ageing alterations in these processes. Vitalii did his PhD jointly supervised by Dr Omer Bayraktar, Dr Oliver Stegle, Dr Sarah Teichmann at Wellcome Sanger Institute (2018-2023) and will present the published and ongoing work. Prior to PhD, Vitalii worked on the role of peptide motifs (SLiMs) in intracellular signaling (Dr Evangelia Petsalaki, EMBL-EBI), predicting CRISR KO mutational outcomes (Dr Leopold Parts, Wellcome Sanger Institute) and profiling protein interactions in accelerated ageing (A*STAR) – while completing MSc and BSc in Kyiv, Ukraine.

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January ABC Seminar: Efrat Muller – Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

January ABC Seminar: Efrat Muller – Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

The human gut microbiome, and its metabolic activity in particular, have been implicated in a wide range of disease states, including metabolic disorders, inflammatory bowel diseases, and colorectal cancer. This growing appreciation for the impact of the gut microbiome’s metabolism on human health has given rise to studies that generate both microbiome and metabolome high-throughput data from human gut microbiome samples. Truly integrated analysis of both omic datasets, however, remains a challenging task. My research aims to develop new frameworks for analyzing and integrating these datasets using a combination of machine learning, metabolic modeling, and network analysis. I’ll specifically present two projects: the first evaluates the robustness of microbiome-metabolome associations using machine learning and meta-analysis models; the second aims to identify “multi-omic modules” that capture both cross-omic associations and associations with disease simultaneously. Taken together, these frameworks can enhance our understanding of microbiome-metabolome connections and equip the microbiome research community with novel methods for such integrated data analysis.

Speaker: Efrat Muller
Affiliation: Prof. Elhanan Borenstein Lab, Tel Aviv University
Position: Doctoral Candidate
Hosted by: Jennifer Dawkins, Gerber Lab

Date: Monday January 22, 2024
Time: 10:00AM-11:00AM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Links:
Google Scholar
Linkedin

Efrat Muller is a Computer Science PhD student studying computational methods for human microbiome research, under the supervision of Professor Elhanan Borenstein at Tel Aviv University. Prior to her PhD, she worked in product management and data analysis positions at Medial EarlySign(a startup in the digital health industry) and at Intel Corporation’s Big Data and Machine Learning group. Efrat completed a B.Sc. and M.Sc. in computer science with honors from Ben-Gurion University. She is generally enthusiastic about any intersection of computer science, machine learning, healthcare and well-being.

Efrat received the EMEA Google Generation Scholarship (2022), Edmond J. Safra Center Excellent Research Student Prize (2022), Naamat scholarship for female PhD students (2022), and prizes for excellence in teaching (Discrete Math course for CS students, 2019-2021).

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December ABC Seminar: Miriam Adler, PhD – Hebrew University

December ABC Seminar: Miriam Adler, PhD – Hebrew University

Fibrosis is a pathology of excessive scarring which causes morbidity and mortality worldwide. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. In this talk, I will introduce a mathematical model we recently developed for a cell circuit between myofibroblasts and macrophages – the two cell types that produce and remodel the scar. The mathematical framework predicts two types of fibrosis – hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. Moreover, we use the model to predict that the autocrine signal for myofibroblast division is a potential therapeutic target to reduce fibrosis. Finally, I will discuss how we use myocardial infarction (MI), a widely studied in-vivo injury model for cardiac fibrosis, to test these theoretical concepts and intervention strategies experimentally.

Speaker: Miriam Adler, PhD
Speaker Affiliation: Alexander Silberman Institute of Life Sciences and the Faculty of Medicine, Hebrew University of Jerusalem
Speaker Position: Senior Lecturer

Date: Monday  December 18, 2023
Time:  10:00am-11:00am ET **New Time**
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866
Hosted by: Utkarsh Sharma, PhD, Gibson Lab

Research Links: https://adlermiri.wixsite.com/mysite
Google Scholar

Miri Adler completed a BSc in Physics at the Technion and obtained an MSc and a PhD in Physics at the Weizmann Institute with Prof. Uri Alon, studying design principles of biological circuits. In her postdoctoral research working jointly with Prof. Ruslan Medzhitov at Yale University and Prof. Aviv Regev at the Broad Institute of MIT and Harvard, Miri developed theoretical frameworks to uncover universal principles of the collective behavior of cells at the tissue level. Miri received a Fulbright scholarship, EMBO postdoctoral scholarship, Zuckerman STEM leadership program fellowship, and the Israel National Postdoctoral Award Program for Advancing Women in Science. Currently she is an associate research scientist at the Tananbaum Center for Theoretical and Analytical Human Biology at Yale University. As of December, she will be a senior lecturer at the Alexander Silberman Institute of Life Sciences and the Faculty of Medicine at the Hebrew University of Jerusalem.

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November ABC Seminar: Gennady Gorin, PhD – CalTech

November ABC Seminar: Gennady Gorin, PhD – CalTech

Stochastic foundations for single-cell RNA sequencing

Single-cell RNA sequencing, which quantifies cell transcriptomes, has seen widespread adoption, accompanied by a proliferation of analytic methods. However, there has been relatively little systematic investigation of its best practices and their underlying assumptions, leading to challenges and discrepancies in analysis. I motivate a set of generic, principled strategies for modeling the biological and technical stochasticity in sequencing experiments, and use case studies to illustrate their prospects for the discovery and interpretation of biophysical kinetics.

Research links:

 

Dr. Gennady Gorin is a chemical engineer working at the exciting intersection of bioinformatics, stochastic biophysics, and statistics. He completed his Ph.D. with Lior Pachter at the California Institute of Technology, adapting theory from fluorescence transcriptomics to the unique features of single-cell RNA sequencing. Prior, he completed a B.S./B.A. at Rice University and performed transcriptional modeling research in the Golding laboratory at Baylor College of Medicine. Gennady is transitioning to industrial bioinformatics, and excited about the prospects for rigorous, physics-informed methods in method development.

All Welcome! Note this event will take place on Zoom.

Date: Monday November 20, 2023
Time: 4:00-5:00PM

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For further information about this seminar series, contact tarnoldmages@bwh.harvard.edu

Staff Scientist (wet/dry) – Gibson Lab

Opening for a Staff Scientist or Senior Staff Scientist to join the Gibson Lab https://gibsonlab.io at Harvard Medical School and Brigham and Women’s Hospital. We leverage tools from machine learning (ML) and dynamical systems theory to better understand biology. The gut microbiome is our primary area of focus at this time where we co-design our experimental and machine learning models adding key experimental components to help validate our models. The scientist in this role will design gnotobiotic mouse experiments, explore the use of new data modalities for studying host-microbiome interactions (e.g. digital ELISA for detection of host cytokines in feces) and will help to design and carry out follow up experiments to validate insights from a our machine learning models. We are obsessed with uncertainty quantification and focus on statistical methods that propagate measurement uncertainty throughout the model so that we can assess confidence in model parameters and to help prioritize follow-up experiments.

Some of our recent funding is below and you will have an active role in expanding our applications beyond those covered here (e.g. gut-brain axis)

Qualifications

  • PhD (although no reason why one can’t be a great scientist without one so its not absolutely necessary)
  • Excellent publication track record
  • Bench scientist at some point in prior life (experience designing and carrying out experiments) while also having the ability to run bioinformatics software
  • Background in microbiology is a plus
  • Ability to reside in the U.S. and legally work in the country

About the lab environment

The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis, and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.

Applications Process

Submit: (1) cover letter; (2) curriculum vitae; (3) relevant publications; (4) names and contact information of three individuals who can serve as references to: Travis Gibson, tegibson@bwh.harvard.edu. If you wish to chat briefly over Zoom before submitting materials to learn more details about our ongoing work, please inquire about this possibility.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Postdoctoral Fellow, Host-Microbiome – Gibson Lab

Opening for a Postdoctoral Research Fellow to join the Gibson Lab https://gibsonlab.io at Harvard Medical School and Brigham and Women’s Hospital. We leverage tools from machine learning (ML) and dynamical systems theory to better understand biology. The gut microbiome is our primary area of focus at this time where we co-design our experimental and machine learning models adding key experimental components to help validate our models. The fellow in this position will explore new aspects of health and disease at the host microbiome interface. All fellows in the Gibson Lab are encouraged to design their own experiments that are then carried out at the mouse facility, but for this project there will be a special emphasis on gnotobiotic experimental design and the exploration of new data modalities for the microbiome. For example, we are investigating the use of digital ELISA to measure low abundance host inflammatory markers in feces (in collaboration with the Walt Lab).

Other projects in the lab include the design of bacteriotherapies (bugs-as-drugs), developing methods to learn microbial dynamics at ecosystem-scale, studying the impact of phages on microbial communities, methods for tracking low abundance pathogens, and methods for integrating multiple data modalities and prior knowledge (from other studies or databases) in time-series models. We focus on Bayesian methods that propagate measurement uncertainty throughout the model so that we can access confidence in model parameters and to help prioritize follow-up experiments. ML techniques applied include variational inference, Bayesian non-parametric models, and relaxation techniques (for making discrete models differentiable).

Qualifications

  • PhD (all dry lab or mixed wet/dry background are preferred, if you have a 100% wet lab background then we can discuss formal appointment with or co-advising by another faculty member in Boston/Cambridge)
  • Solid programming skills in Python; this isn’t a software engineering job by any stretch, but you will need to be able to develop efficient implementations and apply your work to real data
  • Excellent publication track record
  • Background in microbiology a plus (but not required)
  • Ability to reside in the U.S. and legally work in the country.

About the lab environment

The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis, and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.

Applications Process

Submit: (1) brief research statement (not to exceed 2 pages); (2) curriculum vitae; (3) two most relevant publications; (4) names and contact information of three individuals who can serve as references to: Travis Gibson, tegibson@bwh.harvard.edu. If you wish to chat briefly over Zoom before submitting materials to learn more details about our ongoing work, please inquire about this possibility.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Postdoctoral Fellow, Microbiome-Host Immune System Interactions – Gerber Lab

The Gerber Lab (http://gerber.bwh.harvard.edu) is a multidisciplinary group at Brigham and Women’s Hospital/Harvard Medical School that develops novel computational models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. The director of the lab, Dr. Georg Gerber, MD, PhD, MPH, uses his unique expertise, combining advanced machine learning method development, medical microbiology, and human pathology, to leverage cutting-edge technologies to tackle scientifically and clinically important problems.

We are looking for an exceptional researcher who will play a major role in a new initiative in the lab to investigate systematically how commensal microorganisms interact with the host immune system. Although host-bacterial interactions have been extensively characterized for some pathogens, much less is known about how commensal bacteria in the microbiome interact with us. The lab will use both experimental systems (e.g., gnotobiotic animals and cell culture) and computational approaches to study various aspects of immune host system-microbiome interactions, including influence on infection/inflammation and immune repertoire development/diversity.

The successful candidate will be highly motivated and creative, taking a lead role in experimental design, execution of experiments, and interpretation of results. This position is a fantastic opportunity for an individual with strong experimental skills to learn about and apply computational techniques, enriched by extensive collaborations with top computational researchers.

Qualifications:

  • PhD in Immunology, Microbiology, or related discipline.
  • Excellent publication track record.
  • Proficiency in cell and molecular techniques including cell culture, flow cytometry, qPCR, and ELISA assays.
  • Proficiency in mouse studies including animal handling, blood collection, necropsy and cell isolation.
  • Superior communication skills and ability to work on multidisciplinary teams.
  • Experience in cellular immunology, including immune cell proliferation is highly desirable.
  • Experience in microbiology, including culture of anaerobic bacteria is highly desirable.
  • Experience with next-generation sequencing library preparation and data analysis is highly desirable.

Environment:  the Gerber Lab is located in the Division of Computational Pathology (http://comp-path.bwh.harvard.edu), which Dr. Gerber heads, at Brigham and Women’s Hospital (BWH) at Harvard Medical School (HMS), and the Massachusetts Host-Microbiome Center (MHMC) (http://metagenomics.partners.org), which Dr. Gerber co-directs. BWH, an HMS affiliated teaching hospital is adjacent to the HMS main quad and is the second largest non-university recipient of NIH research funding. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. The MHMC has extensive facilities to support microbiome research, including the largest not-for-profit gnotobiotic mouse facility in New England, a microbiology unit with advanced anaerobic culturing systems, and a molecular unit with next generation sequencers and robotic liquid handlers. BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes HMS, Harvard School of Public Health and Boston Children’s Hospital.

To apply: email a single PDF including cover letter, CV, brief research statement and a list of at least three references to Dr. Georg Gerber (ggerber@bwh.harvard.edu). In your CV, indicate whether you are a U.S. citizen/permanent resident or visa holder (and list visa type). Incomplete applications will be considered non-responsive and unfortunately cannot be considered.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

This work will leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. New deep learning models will be developed that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. Computational models and biological experiments will be directly coupled through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. An important objective will also be to make computational tools widely available to the research community, through release of quality open-source software.

RePORTER Link

 

2023-24 ABC Seminar Speakers Announced

2023-24 ABC Seminar Speakers Announced

Developed by the Computational Pathology Division at Brigham and Women’s Hospital, the Advanced Biomedical Computation (ABC) Seminar, held monthly during the academic year, showcases innovative research from around the globe by up-and-coming investigators that are developing and applying advanced computational methods to solve biomedical problems.

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