Immune Cell Social Network Analysis and Visualization

Project Description

Our most pressing problem is the generation of a immune cell network, combining several -omics data sets to examine cellular interactions.

Project Goal:  The project will combine three different biological measurement types examining protein and gene expression on populations and individual cells, use this data to examine how transcriptional control can be examined as “internal sentiments” affecting the protein expression (translational control) and thus interaction between cells in different states (resting, activated, diseased).  Thus, the project will combine transcriptome, proteome, and interactome data -- creating a model of steady state of these in order to examine, and predict the effect of perturbations in the larger immune cell network.  

Research Project Summary: We will generate a framework for immune health by creating a social network of immune cells, modeling both internal state and interactions with other cells. To do this, we will analyze and combine data sets including (1) fluorescence-activated cell sorted cell subsets (FACS) analyzed with high-resolution mass-spectrometry-based proteomics and (2) fluorescence-activated cell sorted cell subsets (FACS) whose whole transcriptome has been analyzed. With (truly) quantitative protein and gene expression data for 28 human hematopoietic cell populations, and more than 10,000 proteins and genes measurements per cell, we generate an interactome for these cells and layer in both transcriptional and translational controls. Using these as internal ‘sentiments’, we will develop a deep steady state social network such that perturbations to the internal state of individual cells (e.g. in the context of disease) can be modeled. Using three examples, (1) CD8 T cell exhaustion, (2) alternate macrophage activation, and (3) dendritic cell dysfunction, we show how perturbations in immune cell gene and protein expression affect the network of immune cells, and ultimately the immune system.

Business Summary: We provide the leading analysis platforms for single cell analysis. 

Expertise Required: 

The ideal candidate or candidate organization will have experience with bioinformatics and a deep understanding of either genomics or proteomics data analysis.  Preference will be given to those candidates with a demonstrated ability to produce high-quality visualizations, experience with networks, graph-based approaches, and machine learning. Candidates with deep lateral experience in social network modeling and analysis but who lack the requisite domain experience will be considered.  

• Demonstrated ability to produce modern, interactive data visualizations (d3.js or equivalent)
• Demonstrated abillity to analyze large biological data sets (R and/or python)
• understanding and experience with bioinformatics analysis (data reduction, Pearson correlation, statistics)
• Demonstrated experience with machine learning (decision trees, ensembles, Logistic Regression)

Data Sources:

Data will primarily be from: Rieckmann et. al. Social network architecture of human immune cells unveiled by quantitative proteomics.  Nature Immunology volume 18, pages 583–593 (2017).  Also available at https://www.nature.com/articles/ni.3693 which includes supplemental information.  (the sample project file is Rieckmann et. al. Supplementary Table 3)

Zheng et. al. Nat Commun. 2017 Jan 16;8:14049 is the source of most of the single cell gene expression data sets.

 

Deliverables: 

There will be three phases of deliverables.  At each sub-step, the deliverable is generally a high-quality visualization as well as derivative data and source for producing the visualization (e.g. scripts).  No integration into our current technology stack is desired.  

Working Model:

• A detailed scope of work which is specified at the level of a requirements document will be provided to prospective candidate or candidate teams.
• Bi-weekly "standup" calls will be established to track progress on the project. 
• Project will be completed in three phases over four weeks by mid-April.