Synthetic symmetry breaking and programmable multicellular structure formation.

During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

Principles for the design of multicellular engineered living systems.

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy.

Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.

Correction: 3D aggregation of cells in packed microgel media.

Correction for '3D aggregation of cells in packed microgel media' by Cameron D. Morley et al., Soft Matter, 2020, DOI: 10.1039/d0sm00517g.

3D aggregation of cells in packed microgel media.

In both natural and applied contexts, investigating cell self-assembly and aggregation within controlled 3D environments leads to improved understanding of how structured cell assemblies emerge, what determines their shapes and sizes, and whether their structural features are stable. However, the inherent limits of using solid scaffolding or liquid spheroid culture for this purpose restrict experimental freedom in studies of cell self-assembly. Here we investigate multi-cellular self-assembly using a 3D culture medium made from packed microgels as a bridge between the extremes of solid scaffolds and liquid culture. We find that cells dispersed at different volume fractions in this microgel-based 3D culture media aggregate into clusters of different sizes and shapes, forming large system-spanning networks at the highest cell densities. We find that the transitions between different states of assembly can be controlled by the level of cell-cell cohesion and by the yield stress of the packed microgel environment. Measurements of aggregate fractal dimension show that those with increased cell-cell cohesion are less sphere-like and more irregularly shaped, indicating that cell stickiness inhibits rearrangements in aggregates, in analogy to the assembly of colloids with strong cohesive bonds. Thus, the effective surface tension often expected to emerge from increased cell cohesion is suppressed in this type of cell self-assembly.

Genomic landscape of cutaneous T cell lymphoma.

Cutaneous T cell lymphoma (CTCL) is a non-Hodgkin lymphoma of skin-homing T lymphocytes. We performed exome and whole-genome DNA sequencing and RNA sequencing on purified CTCL and matched normal cells. The results implicate mutations in 17 genes in CTCL pathogenesis, including genes involved in T cell activation and apoptosis, NF-κB signaling, chromatin remodeling and DNA damage response. CTCL is distinctive in that somatic copy number variants (SCNVs) comprise 92% of all driver mutations (mean of 11.8 pathogenic SCNVs versus 1.0 somatic single-nucleotide variant per CTCL). These findings have implications for new therapeutics.