Sharing advanced technology: core facilities in therapeutics

Therapeutics research requires robust resources for imaging, molecular profiling, informatics, screening, chemistry and medical biochemistry. The LSP has recruited and trained teams of scientists across disciplines organized into a Technology Platforms Cluster (TPC) in Genomic sequencing, Mass-spec proteomics, Quantitative imaging, Bioinformatics and Modeling. These Platforms enable LSP investigators and affiliates to complete large, complex experiments in a timely manner with full confidence in the quality of the data, and support for data integrity and analysis through computational models appropriate to interrogating their hypotheses.

Genomic sequencing: measuring transcriptional state at the population and single cell levels

The Genomic Sequencing Platform supports the most up to date RNA-Seq technologies to transcriptionally profile cell populations and single cells.

Mass-spec proteomics: quantifying proteomic levels in cell populations for discovery and targeted profiling

The Proteomics Platform provides information on the composition of cell lysates, serum and other biological samples using TMT Shotgun Proteomics and focused TOMAHAQ mass spectrometry. Selected samples can be subjected to deep profiling by phopsho-proteomics.

Quantitative imaging: rapid, deep phenotyping of single cells via highly multiplexed cyclic immunofluorescence

The Quantitative Imaging Platform collects information on states and morphologies of cells at high resolution. Samples are routinely subjected to rapid, and inexpensive analysis using high content imaging via staining cells with multiple dyes to score shape and morphology in multiple channels. More thorough phenotyping at a single cell level can be performed using cyclic immunofluorescence (CycIF).

Bioinformatics and Modeling

The Bioinformatics and Modeling Platform provides support for experimental design and subsequent data analysis. The main goal is to develop a systematic framework for biomarker identification, biological interpretation, and hypothesis generation. A subgoal of this effort is to bridge the expertise gap between computational scientists and experimental biologists to facilitate a coherent iterative cycle of data generation and analysis.