Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Functionalized Lineage Tracing for the Study and Manipulation of Heterogeneous Cell Populations.

The ability to track and isolate unique cell lineages from large heterogeneous populations increases the resolution at which cellular processes can be understood under normal and pathogenic states beyond snapshots obtained from single-cell RNA sequencing (scRNA-seq). Here, we describe the Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT) method in which unique single guide RNA (sgRNA) barcodes are used as functional tags to identify and recall specific lineages of interest. An sgRNA barcode is stably integrated and actively transcribed, such that all cellular progeny will contain the parental barcode and produce a functional sgRNA. The sgRNA barcode has all the benefits of a DNA barcode and added functionalities. Once a barcode pertaining to a lineage of interest is identified, the lineage of interest can be isolated using an activator variant of Cas9 (such as dCas9-VPR) and a barcode-matched sequence upstream of a fluorescent reporter gene. CRISPR activation of the fluorescent reporter will only occur in cells producing the matched sgRNA barcode, allowing precise identification and isolation of lineages of interest from heterogeneous populations.

Applications of high-resolution clone tracking technologies in cancer.

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and non-genetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of non-genetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

A Defined and Scalable Peptide-Based Platform for the Generation of Human Pluripotent Stem Cell-Derived Astrocytes.

Astrocytes comprise the most abundant cell type in the central nervous system (CNS) and play critical roles in maintaining neural tissue homeostasis. In addition, astrocyte dysfunction and death has been implicated in numerous neurological disorders such as multiple sclerosis, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD). As such, there is much interest in using human pluripotent stem cell (hPSC)-derived astrocytes for drug screening, disease modeling, and regenerative medicine applications. However, current protocols for generation of astrocytes from hPSCs are limited by the use of undefined xenogeneic components and two-dimensional (2D) culture surfaces, which limits their downstream applications where large-quantities of cells generated under defined conditions are required. Here, we report the use of a completely synthetic, peptide-based substrate that allows for the differentiation of highly pure populations of astrocytes from several independent hPSC lines, including those derived from patients with neurodegenerative disease. This substrate, which we demonstrate is compatible with both conventional 2D culture formats and scalable microcarrier (MC)-based technologies, leads to the generation of cells that express high levels of canonical astrocytic markers as well as display properties characteristic of functionally mature cells including production of apolipoprotein E (ApoE), responsiveness to inflammatory stimuli, ability to take up amyloid-ß (Aß), and appearance of robust calcium transients. Finally, we show that these astrocytes can be cryopreserved without any loss of functionality. In the future, we anticipate that these methods will enable the development of bioprocesses for the production of hPSC-derived astrocytes needed for biomedical research and clinical applications.

An integrated biomanufacturing platform for the large-scale expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells.

Human pluripotent stem cell derived neural progenitor cells (hNPCs) have the unique properties of long-term in vitro expansion as well as differentiation into the various neurons and supporting cell types of the central nervous system (CNS). Because of these characteristics, hNPCs have tremendous potential in the modeling and treatment of various CNS diseases and disorders. However, expansion and neuronal differentiation of hNPCs in quantities necessary for these applications is not possible with current two dimensional (2-D) approaches. Here, we used a fully defined peptide substrate as the basis for a microcarrier (MC)-based suspension culture system. Several independently derived hNPC lines were cultured on MCs for multiple passages as well as efficiently differentiated to neurons. Finally, this MC-based system was used in conjunction with a low shear rotating wall vessel (RWV) bioreactor for the integrated, large-scale expansion and neuronal differentiation of hNPCs. Overall, this fully defined and scalable biomanufacturing system will facilitate the generation of hNPCs and their neuronal derivatives in quantities necessary for basic and translational applications. STATEMENT OF SIGNIFICANCE: In this work, we developed a microcarrier (MC)-based culture system that allows for the expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells (hNPCs) under defined conditions. In turn, this MC approach was implemented in a rotating wall vessel (RWV) bioreactor for the large-scale expansion and neuronal differentiation of hNPCs. This work is of significance as it overcomes current limitations of conventional two dimensional (2-D) culture systems to enable the generation of hNPCs and their neuronal derivatives in quantities required for downstream applications in disease modeling, drug screening, and regenerative medicine.