LSP Data and Methods

The Harvard Tissue Atlas (HTA) assembles image and -omic datasets into precise 3D molecular maps of cell types, states, and interactions between cells and acellular structures. Our atlases bring together multiple research projects examining normal and diseased tissue from human and animal models, with an emphasis on cancer. We aim to better understand disease initiation and development to enable the next generation of diagnostic molecular tests and precision medicine approaches.

CyCIF (cyclic immunofluorescence) is a robust and inexpensive method for highly multiplexed immunofluorescence imaging using standard instruments and reagents. Tissue-based (t-) CyCIF generates multiplexed images of fixed, paraffin-embedded (FFPE) samples using an iterative process in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. Highly multiplexed images of intact tumor architecture can be used to quantify signal transduction cascades, measure the levels of tumor antigens, determine precise immune phenotypes, and more. 

Orion is a method for collecting one-shot 18-plex immunofluorescence images and diagnostic-grade H&E images from the same samples. The Orion method was developed in collaboration with RareCyte Inc. and uses a specialized microscope and fluorescent antibodies (known as ArgoFluors™), which can be imaged simultaneously and spectrally unmixed. We show that same-slide H&E and IF images provide complementary information that can be used to train ML models that effectively predict cancer progression.  

The Dye Drop assay is a versatile, low-cost, multiplexed microscopy method for obtaining detailed single-cell viability and cell cycle data. The Dye Drop method uses a set of incrementally more dense solutions to prevent cell loss and improve the consistency of cell perturbation experiments. Dye Drop can also be combined with CyCIF to yield further molecular and spatial information. The method is paired with computational analysis tools that calculate cell state and growth rate metrics from the high throughput data.

The HMS LINCS Database is a public resource for browsing, searching, and downloading all fully annotated datasets generated through the HMS LINCS Center along with experimental and analytical methods. The database contains >350 datasets from imaging, transcriptomic, and proteomic experiments as well as in vitro small molecule affinity assays.

The Growth Rate inhibition (GR) Calculator (grcalculator.org) is an open source set of Python, R and online tools for quantifying the responses of cancer cells to drugs in a manner that corrects for the confounding effects of variable cell proliferation rates. Response metrics computed from GR data include GR50 and GRmax and are direct analogs of familiar IC50 and Emax response measures.

ProteinNet is a standardized data set for machine learning of protein structure. It provides protein sequences, structures (secondary and tertiary), multiple sequence alignments (MSAs), position-specific scoring matrices (PSSMs), and standardized training / validation / test splits. ProteinNet builds on the biennial CASP assessments, which carry out blind predictions of recently solved but publicly unavailable protein structures, to provide test sets that push the frontiers of computational methodology. It is organized as a series of data sets, spanning CASP 7 through 12 (covering a ten-year period), to provide a range of data set sizes that enable the assessment of new methods in relatively data-poor and data-rich regimes.

DRIAD (Drug Repurposing In Alzheimer’s Disease) is a machine learning framework for identifying potential associations between Alzheimer’s Disease pathology and individual genes. DRIAD was applied to data from perturbation experiments performed in the LSP using differentiated human neural cell cultures and 80 FDA-approved and clinically tested drugs, to nominate existing drugs that could be potentially be repurposed for Alzheimer’s treatment. 

CancerTrials.io contains imputed individual participant data (IPD) from previously published oncology research clinical trials (RCTs). The site also contains various types of analysis performed on these data.

DUB (Deubiquitinating enzymes) portal provides the integration and analysis of publicly available resources as well as newly collected transcriptomic data following DUB knockout or inhibition to facilitate the exploration of DUB function in oncology.