Build-a-Cell: Engineering a Synthetic Cell Community

Build-a-Cell is a global network of researchers that aims to develop synthetic living cells within the next decade. These cells will revolutionize the biotechnology industry by providing scientists and engineers with a more complete understanding of biology. Researchers can already replicate many cellular functions individually, but combining them into a single cell remains a significant challenge. This integration step will require the type of large-scale collaboration made possible by Build-a-Cell’s open, collective structure. Beyond the lab, Build-a-Cell addresses policy issues and biosecurity concerns associated with synthetic cells. The following review discusses Build-a-Cell’s history, function, and goals.

825 High-dimensional image cytometry reveals spatially organized tumor-immune microenvironment in hepatocellular carcinoma

BackgroundStructured and spatial-nuanced interactions between components in tumor microenvironment (TME) regulates the efficacy of anti-tumor regimens. Insights into this orchestrated behavior in therapeutic responders and non-responders will facilitate immunotherapies. High-multiplex imaging and spatial statistics enable deep profiling of TMEs by simultaneous arraying cell phenotypes and locations. In this study, we quantified the landscape of TMEs from neoadjuvant cabozantinib and nivolumab administered locally advanced hepatocellular carcinoma (HCC) biospecimen.Methods14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMAs) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers (figure 1). All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding technology. Results from these hierarchies are merged to response-associated biological process patterns.ResultsImage processing on 37 multiplexed images discriminated 59,453 cells and then clustered into 17 cell types. Multi-level spatial quantification revealed distinct TME arrangements across cores from responders (R) and non-responders (NR): compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B level is lower (figure 2B). We also identified the proximity of CD8+ T cells to a subset of macrophages – Arginase 1hi CD163- macrophages (hazard macrophage) and CD4+ T cells, is a prognostic biomarker to neoadjuvant therapy (figure 3A and 3B). In-depth cell community analysis extracted cell-cell interaction networks based on spatial proximity. Next, hierarchical clustering grouped all networks with similar components (cell types) into 8 community categories (CC). Using graph-embedding and correlation test, we observed that in NR, macrophage-enriched CC (MCC) and lymphocytes-enriched CC (LCC) are strongly communicating with tumor CC; whereas in R, such communications were weakened by the engagement between MCC and LCC (figure 3C).ConclusionsIn conclusion, we employed an unbiased, quantitative spatial analysis to determine how tumor and immune components interact in responding and nonresponding HCC tumors. Based on our results, four immune-regulating factors are derived and summarized as a communication landscape (figure 4). The proposed framework represents a novel application of multiplexed imaging in translational medicine and has potential in initialization and validation of computational immuno-oncology models.AcknowledgementsThe authors acknowledge financial support from Bristol-Myers Squibb, Exelixis, the National Cancer Institute Specialized Program of Research Excellence (SPORE) in Gastrointestinal Cancers (P50 CA062924), the Passano Foundation, the National Institutes of Health (Grant No. U01CA212007 and R01CA138264) and Emerson Collective Cancer Research Fund (640183).Ethics ApprovalThe studies involving human participants were reviewed and approved by Institutional Review Board of the Johns Hopkins Medical Institutions.ConsentWritten informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.Abstract 825 Figure 1A panel of 27 markers was used to stain the hepatocellular carcinoma tumor region cores and processed using IMC. The marker names and descriptions are includedAbstract 825 Figure 2(A) Color overlays of lineage proteins covering Pan-Keratin and CD45 (rainbow) and functional markers covering PD-L1 and Granzyme B (white) in whole tissue core and subregions. (B) and (C) Protein expression analytical strategy. For compartmentalized cores, functional marker expressions on target cells were examined adjacent and remote to tumor-immune border and truncated to treatment response criteria for comparisonsAbstract 825 Figure 3(A) Diagram of CD8+ T cell RiskScore. Denote each CD8+ T cell to its nearest hazard macrophages as d1 and to its nearest CD4+ T cell as d2, thus the RiskScore is formally computed by taking the proportion of d2 to the combined distance of d1 and d2. (B) RiskScore on per-cell basis for responders and non-responders. (C) Cell community communication maps in tumor microenvironment associated with responders and non-respondersAbstract 825 Figure 4The synergistic anti-tumor immunity of macrophages and lymphocytes favors cabozantinib and nivolumab; immune function regulators (i.e., GranB and PD-L1) were upregulated throughout the immune compartment in non-responders; close proximity to hazard macrophages and distance away from CD4+ T cells associate with poorer effector function of CD8+ T cells

China’s Stem Cell Research and Knowledge Levels of Medical Practitioners and Students

Over the last few decades, China has greatly expanded its scope of stem cell research, generating various scientific advances and medical applications. However, knowledge of the extent and characteristics of domestic stem cell development, particularly medical workers’ opinions, is lacking. This study’s purposes were to analyze the growth trends of China’s stem cell community and identify the knowledge and attitudes held by Chinese medical workers regarding stem cell research. We found that there are currently 13 high-quality stem cell research centers with more than 400 PhD-level researchers across Mainland China. These centers feature many high-caliber scientists from the stem cell research community. From 1997 through 2019, the National Natural Science Foundation of China allocated roughly $576 million to 8,050 stem cell programs at Chinese universities and research institutions. China’s annual publications on stem cells increased from less than 0.6% of the world’s total stem cell publications in 1999 to more than 14.1% in 2014. Our survey also revealed that most participants held positive attitudes toward stem cell research, supported further funding, and had high general awareness about stem cells.

Stem Cell Neurodevelopmental Solutions for Restorative Treatments of the Human Trunk and Spine

The ability to reliably repair spinal cord injuries (SCI) will be one of the greatest human achievements realized in regenerative medicine. Until recently, the cellular path to this goal has been challenging. However, as detailed developmental principles are revealed in mouse and human models, their application in the stem cell community brings trunk and spine embryology into efforts to advance human regenerative medicine. New models of posterior embryo development identify neuromesodermal progenitors (NMPs) as a major bifurcation point in generating the spinal cord and somites and is leading to production of cell types with the full range of axial identities critical for repair of trunk and spine disorders. This is coupled with organoid technologies including assembloids, circuitoids, and gastruloids. We describe a paradigm for applying developmental principles towards the goal of cell-based restorative therapies to enable reproducible and effective near-term clinical interventions.

Autotoxin-mediated voluntary triage in starved yeast community

When organisms face crises, such as starvation, every individual should adapt to environmental changes (1, 2), or the community alters their behaviour (3–5). Because a stressful environment reduces the carrying capacity (6), the population size of unicellular organisms shrinks in such conditions (7, 8). However, the uniform stress response of the cell community may lead to overall extinction or severely damage their entire fitness. How microbial communities accommodate this dilemma remains poorly understood. Here, we demonstrate an elaborate strategy of the yeast community against glucose starvation, named the voluntary triage. During starvation, yeast cells release some autotoxins, such as leucic acid and L-2keto-3methylvalerate, which can even kill the cells producing them. Although it may look like mass suicide at first glance, cells use epigenetic “tags” to adapt to the autotoxin inheritably. If non-tagged latecomers, regardless of whether they are closely related, try to invade the habitat, autotoxins kill them and inhibit their growth, but the tagged cells can selectively survive. Phylogenetically distant fission and budding yeast (9) share this strategy using the same autotoxins, which implies that the universal system of voluntary triage may be relevant to the major evolutional transition from unicellular to multicellular organisms (10).

Bacterial glycocalyx integrity drives multicellular swarm biofilm dynamism

ABSTRACTBacterial surface exopolysaccharide (EPS) layers are key determinants of biofilm establishment and maintenance, leading to the formation of higher-order 3D structures conferring numerous survival benefits to a cell community. In addition to a specific EPS glycocalyx, we recently revealed that the social δ-proteobacterium Myxococcus xanthus secretes a novel biosurfactant polysaccharide (BPS), with both EPS and BPS polymers required for type IV pilus (T4P)-dependent swarm expansion via spatio-specific biofilm expression profiles. Thus the synergy between EPS and BPS secretion somehow modulates the multicellular lifecycle of M. xanthus. Herein, we demonstrate that BPS secretion functionally-activates the EPS glycocalyx via its destabilization, fundamentally altering the characteristics of the cell surface. This impacts motility behaviours at the single-cell level as well as the aggregative capacity of cells in groups via EPS fibril formation and T4P assembly. These changes modulate structuration of swarm biofilms via cell layering, likely contributing to the formation of internal swarm polysaccharide architecture. Together, these data reveal the manner by which the interplay between two secreted polymers induces single-cell changes that modulate swarm biofilm communities.

UMAP does not preserve global structure any better than t-SNE when using the same initialization

One of the most ubiquitous analysis tools employed in single-cell transcriptomics and cytometry is t-distributed stochastic neighbor embedding (t-SNE) [1], used to visualize individual cells as points on a 2D scatter plot such that similar cells are positioned close together. Recently, a related algorithm, called uniform manifold approximation and projection (UMAP) [2] has attracted substantial attention in the single-cell community. In Nature Biotechnology, Becht et al. [3] argued that UMAP is preferable to t-SNE because it better preserves the global structure of the data and is more consistent across runs. Here we show that this alleged superiority of UMAP can be entirely attributed to different choices of initialization in the implementations used by Becht et al.: t-SNE implementations by default used random initialization, while the UMAP implementation used a technique called Laplacian eigenmaps [4] to initialize the embedding. We show that UMAP with random initialization preserves global structure as poorly as t-SNE with random initialization, while t-SNE with informative initialization performs as well as UMAP with informative initialization. Hence, contrary to the claims of Becht et al., their experiments do not demonstrate any advantage of the UMAP algorithm per se, but rather warn against using random initialization.