Research

In addition to harnessing spatial profiling techniques with the scientific community, the Spatial Technology Platform performs academic research in the areas of biomedicine, technology development, and computation. The platform is composed of four sub-groups, led by the platform director and senior scientists, each with unique scientific interests.

Farhi group

Research in our lab focuses on pushing the boundary of imaging technologies, seeking ever more comprehensive and multi-modal portraits of biological systems.

The links below describe some ongoing projects.

Identifying gene modules underlying psychiatric disease

Human genetic studies are finding an ever-growing number of variants associated with psychiatric disease such as Autism Spectrum Disorder and Schizophrenia. Our lab seeks to understand the neurobiological consequences of these variants with high-content measurements of human Pluripotent Stem Cell Derived Neurons (hPSC-Ns). Our approach is to: Generate pools of hPSC-Ns of hundreds of genetic backgrounds, either using natural patient variability or experimentally induced targeted changes with CRISPR-Cas9. Measure rich (>100's of parameters) fingerprints of thousands of these cells' transcriptomes, electrophysiological properties, and morphological structure with newly developed optical tools. Computationally integrate the resulting data sets to generate gene set annotations associated with basic neuronal function and their change in disease genetic backgrounds. The results of this work will serve to identify common cellular mechanisms in disease states for therapeutic intervention; implement scalable assays for further therapeutic development; and close the circle to further power genetic studies for additional discovery. The work occurs alongside, and benefits from, ongoing tool design efforts occurring in the platform, and is a close collaboration with Dr. Ralda Nehme's lab and with other members of the Stanley Center for Psychiatric Research.

Increasing imaging transcriptomics throughput

In Imaging Transcriptomics methods, such as MERFISH, STARmap, and SEQfish, samples are passed through multiple rounds of multi-color imaging with single-molecule resolution, and the sequence of colors originating from individual molecules is used to assign each molecule to a gene identity, encoded by a molecularly designed codebook. This is done with single molecule resolution, and thus analyzing an organ like an entire mouse brain with IT would require years of instrument time. A human brain atlas would require thousands of times longer. We approach this problem by considering the structure of the data and finding inefficiencies in the sampling strategy. We then develop compressed sensing and machine learning methods to recover the same information from more efficiently sampled data. Finally, we implement these methods in the form of newly designed optical systems and biochemical strategies. This work is performed in close collaboration with Dr. Brian Cleary at the Broad and Dr. Yonina Eldar at the Weizmann Institute.

Generation of mammalian tissue atlases

Where single cell sequencing revolutionized our understanding of tissues by building cellular “parts-lists,” spatial transcriptomics and proteomics stand to reveal how these parts fit and function together as one system. We use these tools to build comprehensive atlases of tissues to identify how disease perturbations induce and perturb cell interactions. Two overarching areas are: Identifying the cell-to-cell interactions during brain development Brain development consists of an integrated dance of cell type differentiation, migration, and subsequent connection pruning. Psychiatric diseases such as autism spectrum disorder and Schizophrenia perturb this process, but the mechanisms involved are still poorly understood. We thus aim to generate unbiased reference maps of the cell type composition over developmental stages of the mouse brain. We use these data to identify interactions critical for normal brain development, and how these interactions may be disrupted in mice with perturbed psychiatric risk genes. Common cellular interactions across immune disease and cancer We generate atlases in both human tumors and in biopsies from immune diseases from multiple tissues at various progression stages and treatment conditions. Taking studies individually, this data helps reveal the cellular interactions underlying immunotherapy response or the scar formation. We aim to leverage our central position across multiple studies to identify cellular interactions that are shared across tissues (i.e. lung and gut) or across injuries (i.e. tumor formation and inflammation).

Lipinski group

Michal Lipinski leads the sequencing-based spatial transcriptomics team within the Spatial Technology Platform. This group focuses on exploring the technological boundaries of the sequencing-based methods and applying them in unique ways to discover novel biology. Michal’s particular interest lies in understanding psychiatric and nervous system disorders by utilizing stem cell models and intact human tissue. These approaches, coupled with next-generation methods for evaluating drugs in the lab, hold promise for accelerating drug discovery and precision medicine.

Publications

Fernandez group

Nicolas Fernandez leads the computational team within the Spatial Technology Platform. Our team is focused on developing tools and best practices for researchers to extract biological insights from single-cell spatial multi-omics data. Nicolas’ research interests include tissue organization, the tumor-immune microenvironment, and developing multimodal spatial analysis/visualization approaches. Nicolas joined the Broad institute in December 2023 and has previously worked as a computational biologist in academia and industry after completing his Ph.D in Biomedical Sciences from Albert Einstein College of Medicine.

Publications

Erion-Barner group

Lindsey Erion-Barner leads the imaging-based spatial transcriptomics team within the Spatial Technology Platform. This group focuses on developing novel approaches for high-throughput and 3D imaging transcriptomics, as well as multi-omic approaches that combine protein labeling and imaging transcriptomics. Additionally, her team is developing optogenetic approaches for investigating mechanisms of psychiatric diseases in collaboration with the Granger group. Her research interests include leveraging and developing new light sheet microscopy techniques for multiplexed 3D transcriptomics and high-throughput imaging methods for multi-omics, as well as 3D imaging techniques for pathology applications.