Mammalian transgene expression relies on a limited collection of natural promoters that drive discrete levels of transcription and consequent protein expression. However, the behaviors of natural promoters could be challenging to predict in different cell types, making it difficult to precisely control transcription. To address these obstacles, using synthetic biology toolkits, we have developed versatile, scalable platforms and programmable genetic components to transform regulation of mammalian transcription and transgene expression. 

To comprehensively understand transcription factors (TFs), we created a barcoded library of all human TF splice isoforms and applied it to build a TF Atlas charting single-cell expression profiles of pluripotent stem cells overexpressing each TF. We validated TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. We further developed a strategy for predicting TF combinations that produce target cell types to accelerate cellular engineering.

We have developed synthetic cancer-targeting gene circuits that specifically target cancer cells. Once the circuits enter cells, they will sense the activity of several cancer-associated transcription factors and get activated in tumor cells to trigger tumor-localized combinatorial immunotherapy. Circuits mediate robust therapeutic efficacy in ovarian cancer mouse models. This platform can be adjusted to treat multiple cancer types and can potentially trigger genetically encodable immunomodulators as therapeutic outputs.

I will be talking on how industry/public partnership can enable the development of functional genomics capabilities for target discovery. I will present a few case studies to exemplify this concept and discuss the challenges and opportunities we face in this field.

CRISPR-Cas9 has been reported to cause a variety of undesired large-scale structural changes to the genome. We performed an arrayed CRISPR-Cas9 scan of the genome in primary human cells targeting 17,065 genes and revealed a proximity bias in which knockouts bear unexpected phenotypic similarity to unrelated genes on the same chromosome arm. Transcriptomics connects this effect to chromosome-arm truncations, and analysis of published large-scale experiments confirms that it is general across cell types, labs, Cas9 delivery mechanisms, and assay modalities. Finally, we demonstrate a simple correction for large-scale CRISPR screens to mitigate this pervasive bias while preserving biological relationships.

Abstract: We will cover CRISPR technologies that allow genome exploration at both broad scale and fine resolution. For the former, Cas12a allows for facile multiplexing of guide RNAs, simplifying combinatorial perturbations; we will share work mapping synthetic lethal interactions with knockout screens and progress in developing Cas12a for CRISPR activation screens. Additionally, base editor technology enables nucleotide-level manipulation, and we will present screens mapping cancer-relevant genes and pathways. 

In recent years, we have seen significant advancements in the field of precision medicine. New genomic technologies hold great promise for the identification of actionable drug targets and associated biomarkers for several complex diseases, such as autoimmunity. Our approach uses single-cell RNAseq and machine learning to elucidate the precise cell types and gene expression programs involved in the progression of complex diseases in order to identify novel therapeutic targets.

In this talk, I will discuss new approaches that we are exploring in combining deep learning, omics, and functional data along with clinical information to predict new drug targets.

Cornerstone to the development of cancer and other age-dependent disease are the downstream influences of systemic metabolism and inflam-ageing. Relying on skin biopsy-derived fibroblasts, we developed multivariate phenotypic readouts that quantify metabolic and inflammatory rates that are specific to the individual biopsy donor at the single cell level. We found that these cell assay readouts correlate with pharmacological (rapamycin) or life-style (weight gain) interventions that had occurred in the individuals past and predict the onset age of cancers in the individuals future. Skin fibroblasts derived from cancer-free individuals from Li Fraumeni patients that were subject to years-long clinical surveillance, predicted the specific (personalized) onset age of cancers that developed years post-biopsy.

Algen is a platform therapeutics and drug discovery company using the world’s leading CRISPR and AI to uncover disease-driving RNA messages to find treatments for cancer, inflammation, and other diseases. Spun out from Nobel Laureate Professor Jennifer Doudna's Lab, Algen aims to develop the world’s smartest drug discovery decision platform and data universe to create next-generation therapeutics.