From a methods perspective, our primary interest is in the development of the data science and machine learning expertise for the analysis of data geneated by single cell profiling assays across different modalities and in different contexts. You can read about these here. We have a long history of working in breast cancer informatics and genomics, and continue to develop analytic methodology in this direction. There are a number of on-going computational projects, many of which are rooted in deep learning.

  1. Analytical techniques to capture cellular response to chemical perturbagens. This work has primarly been done in the context of our fungal C. albicans projects. The work is based on extensions to GAN and VAEs entities from deep learning.

  2. Computational vision tools for mycology. This work is primarily based on extensions to FASTER R-CNN models.

  3. Methods to exploit multi-modal single cell data. This is ubiquitous throughout our projects, and relies heavily on basic statistics and visualization through deep representations.

  4. The development of gene and gene product signatures related to clincial endpoints primarily in breast cancer. See for example our CIHR-funded DCIS project.

  5. The inference and representation of biological networks. See for example this.

This work has been developed with different members of our team.

The wet lab portion of our group develops single cell profiling assays across different modalities and in different contexts. Our goal is not necessarily to advance genomics; rather we are focused on generating the right kind of data with good experimental design and an affordable cost. Although we do work with commerical systems including, for example, the Chromium system (10X Genomics Inc), we are also interested in engineering these systems ourselves. Although our lab does not offer a service per se, we collaborate with many groups in a research context with our assays described belief below.

  1. Transcriptional profiling via sc-DROP-seq as per Macosko et al

  2. Transcriptional profiling via various modified sc-DROP-seqs inspired by Stephenson et al. and Booeshaghi et al. for self-design and printing of components.

  3. We recently developed a fungal DROP-seq bioRxiv The main differences between fungi and mammalian cells is that fungi have a cell wall that needs to be removed before they are lysed, and they have considerably less mRNA; with Samira Massahi and Van Bettauer.

  4. DART-seq Saikia et al. is a modified DROP-seq protocol that allows for simultaneous multiplexed amplicon sequencing and transcriptome profiling of single cells.

  5. DRONC-seq Habib et al. is a DROP-seq based method to sequecing individual nuclei; we have been working to get this running in FFPE tumor material; Samantha Yuen.

  6. sc-snare-seq2 Chen et al., a DROP-seq approach that profiles both the transcriptome and chromatin accessibility of individual cells; with Vanessa Dumeaux and Suhani Patel.

  7. We are in the process of setting up several types of sci-SPLIT-seq Cao et al and PETRI-seq Blattman et al approaches based on barcoding of cells without microfluidics; with Brandon Finlay, Shawn Simpson.

  8. We are developing PERTURB-seq in mammalian systems. This work expands on many papers including the seminal manuscripts from Adamson et al and Dixit et al.

All of this work is supported by bioinformatic pipelines for the basic processing and quality control of the resultant data. Our primary interest as a group however is in developing the data science expertise for the deep analysis of data towards hypotheses specific to each project. This includes methods from statsitics, computational, data science, and deep learning.

This work has been developed with different members of our team but very often in collaboration with Vanessa Dumeaux at PERFORM, Concordia, and a broad range of additional collaborators.

We recently modified the DROP-seq protocol from Macosko et al to function with the fungus C. albicans. The main differences between fungi and mammalian cells are that the former have a cell wall that needs to be removed before lysis, and the latter have considerably more mRNA. This system was used to profile C. albicans grown in the presence of different antifungal drugs. We observed heterogeneity in their response, with evidence of distinct subpopulations of survivors with differential survival responses.

bioRxiv preprint

Samira Massahi, and Van Bettauer (computational biology) are leads on these projects.

This is joint work with Malcolm Whiteway and Vanessa Dumeaux at Concordia.

We recently collected samples of water and small samples of coral from two locations in Barbados. One location has a relatively healthy reef whereas the other location has been in steady decline. We performed deep sequencing to identify the microbiomes at both sites and are working with collaborators to develop this into a national project to monitor reef health and determine the external influences threatening their health across the island’s fringing reef system.

Shawn Simpson is the lead on this project.

This is joint work with David Walsh at Concordia, Henri Vallès, Yvonne Vallès at UWI Cavehill, Barbados and others including the CORALL society, Barbados.

We have other microbiome projects with Vanessa Dumeaux related to human health and breast cancer.

Our lab has several projects related to the inference of biological networks. We have developed approaches that perturb specific sets of genes or transcripts using genome editing (eg CRISPR) in a high-throughput, multivariate pseudo-random manner. The edits can be knockouts, knock-downs or over-expression of the targets, or a combination thereof. Single cell high-throughput profiling of transcription, select proteins, or chromosomal conformations are used to deduce underlying biological networks - the regulatory and correlative relationships between genes and gene products.

These projects bring together all the components of our lab:

  • Single cell multi-modal -omic profiling;
  • Genome editing;
  • Next generation sequencing;
  • Data science and analysis of high-throughput data; and
  • Deep learning for inference of biological networks.

Sanny Khurdia, Aki Kirbizakis and Van Bettauer (computational biology) are leads on these projects. We are also lucky to have Aaybod assisting as part of his genomics diploma.

We apply this to different biological systems including in the context of breast cancer with Sylvie Mader’s group at the IRIC/UdeM.

This is a collaboration with Vanessa Dumeaux at the PERFORM Centre, Concordia, and many others including researchers and clinicians at IRIC, Sunnybrook and the CHUM.

Our group has a long history of working in breast cancer genomics and informatics. Currently, we are investigating the role of the immune system in HER2+ breast cancers, with an emphasis on characterizing the immunological systemic response of patients, and how chages during their course of treatment.

We receive support the the Office of the VP-Research at Concordia, and other funding agencies.

This is a CIHR funded project. It is a collaboration with Eileen Rakowitch at the Sunnybrook Hospital in Toronto, Canada.

Eileen is a radiotherapist who had great foresight and energy to build a cohort tumor banking of many DCIS cases in Ontario from 1993 until present.

This cohort is very well annotated with patient, clinical and pathological information regardin the lesion and patient.

Ductal in situ carcinoma (DCIS) is a very common form of breast lesions background.

DCIS is a non-obligate precursor to invasive “life-threatening” breast cancer invasive ductal carcinoma, IDC.

That is, no all DCIS will become invasive if left untreated (or at least not within the natural lifetime of an individual).

In constrast to such indolent DCIS, some such lesions do progress, escaping from the mammary duct, into the surrounding breast tissue and, if left untreated, to other tissues and organs metastases.

A woman with an indolent DCIS might decide in consultation with her health care practioners towards a milder form of therapy involving on breast conserving surgery BCS

If they suspect she has a more agressive DCIS, then treatment might include BCS with radiotherapy.

The problem is that we have no way currently to decide whether a woman with DCIS benefits from the additional radiotherapy.

Therefore current clinical practice is cautious and leans towards the inclusion of radiotherapy.

We are interested in identifying a molecular signature that can predict whether a woman with DCIS would benefit from a regime with only BCS or BCS + raditiotherapy.

To do this, we are profiling Eileen’s Ontario cohorts using next generation sequencing and analysis methods from computational biology.

This data and analyses allow us to glimpse how each such lesion evolved, somatic DNA events and changes in the regulation and expression of specific pathways.

From this data, we are developing molecular signatures that have the ability to predict at time of diagnosis the benefit from the inclusion of radiotherapy to assist clinical decision making.