Predicting cell lineages using autoencoders and optimal transport.

Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used.

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq.

E3 ligases regulate key processes, but many of their roles remain unknown. Using Perturb-seq, we interrogated the function of 1,130 E3 ligases, partners and substrates in the inflammatory response in primary dendritic cells (DCs). Dozens impacted the balance of DC1, DC2, migratory DC and macrophage states and a gradient of DC maturation. Family members grouped into co-functional modules that were enriched for physical interactions and impacted specific programs through substrate transcription factors. E3s and their adaptors co-regulated the same processes, but partnered with different substrate recognition adaptors to impact distinct aspects of the DC life cycle. Genetic interactions were more prevalent within than between modules, and a deep learning model, comßVAE, predicts the outcome of new combinations by leveraging modularity. The E3 regulatory network was associated with heritable variation and aberrant gene expression in immune cells in human inflammatory diseases. Our study provides a general approach to dissect gene function.

Multi-domain translation between single-cell imaging and sequencing data using autoencoders.

The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing.

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

Expanded Satellite Repeats Amplify a Discrete CENP-A Nucleosome Assembly Site on Chromosomes that Drive in Female Meiosis.

Female meiosis provides an opportunity for selfish genetic elements to violate Mendel's law of segregation by increasing the chance of segregating to the egg [1]. Centromeres and other repetitive sequences can drive in meiosis by cheating the segregation process [2], but the underlying mechanisms are unknown. Here, we show that centromeres with more satellite repeats house more nucleosomes that confer centromere identity, containing the histone H3 variant CENP-A, and bias their segregation to the egg relative to centromeres with fewer repeats. CENP-A nucleosomes predominantly occupy a single site within the repeating unit that becomes limiting for centromere assembly on smaller centromeres. We propose that amplified repetitive sequences act as selfish elements by promoting expansion of CENP-A chromatin and increased transmission through the female germline.

Spindle asymmetry drives non-Mendelian chromosome segregation.

Genetic elements compete for transmission through meiosis, when haploid gametes are created from a diploid parent. Selfish elements can enhance their transmission through a process known as meiotic drive. In female meiosis, selfish elements drive by preferentially attaching to the egg side of the spindle. This implies some asymmetry between the two sides of the spindle, but the molecular mechanisms underlying spindle asymmetry are unknown. Here we found that CDC42 signaling from the cell cortex regulated microtubule tyrosination to induce spindle asymmetry and that non-Mendelian segregation depended on this asymmetry. Cortical CDC42 depends on polarization directed by chromosomes, which are positioned near the cortex to allow the asymmetric cell division. Thus, selfish meiotic drivers exploit the asymmetry inherent in female meiosis to bias their transmission.

Spatial Regulation of Kinetochore Microtubule Attachments by Destabilization at Spindle Poles in Meiosis I.

To ensure accurate chromosome segregation in cell division, erroneous kinetochore-microtubule (MT) attachments are recognized and destabilized . Improper attachments typically lack tension between kinetochores and are positioned off-center on the spindle. Low tension is a widely accepted mechanism for recognizing errors , but whether chromosome position regulates MT attachments has been difficult to test. We exploited a meiotic system in which kinetochores attached to opposite spindle poles differ in their interactions with MTs and therefore position and tension can be uncoupled. In this system, homologous chromosomes are positioned off-center on the spindle in oocytes in meiosis I, while under normal tension, as a result of crossing mouse strains with different centromere strengths, manifested by unequal kinetochore protein levels. We show that proximity to spindle poles destabilizes kinetochore-MTs and that stable attachments are restored by inhibition of Aurora A kinase at spindle poles. During the correction of attachment errors, kinetochore-MTs detach near spindle poles to allow formation of correct attachments. We propose that chromosome position on the spindle provides spatial cues for the fidelity of cell division.

EBNA3C regulates p53 through induction of Aurora kinase B.

In multicellular organisms p53 maintains genomic integrity through activation of DNA repair, and apoptosis. EBNA3C can down regulate p53 transcriptional activity. Aurora kinase (AK) B phosphorylates p53, which leads to degradation of p53. Aberrant expression of AK-B is a hallmark of numerous human cancers. Therefore changes in the activities of p53 due to AK-B and EBNA3C expression is important for understanding EBV-mediated cell transformation. Here we show that the activities of p53 and its homolog p73 are dysregulated in EBV infected primary cells which can contribute to increased cell transformation. Further, we showed that the ETS-1 binding site is crucial for EBNA3C-mediated up-regulation of AK-B transcription. Further, we determined the Ser 215 residue of p53 is critical for functional regulation by AK-B and EBNA3C and that the kinase domain of AK-B which includes amino acid residues 106, 111 and 205 was important for p53 regulation. AK-B with a mutation at residue 207 was functionally similar to wild type AK-B in terms of its kinase activities and knockdown of AK-B led to enhanced p73 expression independent of p53. This study explores an additional mechanism by which p53 is regulated by AK-B and EBNA3C contributing to EBV-induced B-cell transformation.