Publication, funding, and experimental data in support of Human Reference Atlas construction and usage.

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

Multiscale topology classifies cells in subcellular spatial transcriptomics.

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Sex differences orchestrated by androgens at single-cell resolution.

Sex differences in mammalian complex traits are prevalent and are intimately associated with androgens1-7. However, a molecular and cellular profile of sex differences and their modulation by androgens is still lacking. Here we constructed a high-dimensional single-cell transcriptomic atlas comprising over 2.3 million cells from 17 tissues in Mus musculus and explored the effects of sex and androgens on the molecular programs and cellular populations. In particular, we found that sex-biased immune gene expression and immune cell populations, such as group 2 innate lymphoid cells, were modulated by androgens. Integration with the UK Biobank dataset revealed potential cellular targets and risk gene enrichment in antigen presentation for sex-biased diseases. This study lays the groundwork for understanding the sex differences orchestrated by androgens and provides important evidence for targeting the androgen pathway as a broad therapeutic strategy for sex-biased diseases.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues.

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

Recording physiological history of cells with chemical labeling.

Recordings of the physiological history of cells provide insights into biological processes, yet obtaining such recordings is a challenge. To address this, we introduce a method to record transient cellular events for later analysis. We designed proteins that become labeled in the presence of both a specific cellular activity and a fluorescent substrate. The recording period is set by the presence of the substrate, whereas the cellular activity controls the degree of the labeling. The use of distinguishable substrates enabled the recording of successive periods of activity. We recorded protein-protein interactions, G protein-coupled receptor activation, and increases in intracellular calcium. Recordings of elevated calcium levels allowed selections of cells from heterogeneous populations for transcriptomic analysis and tracking of neuronal activities in flies and zebrafish.

Gravity-Oriented Microfluidic Device for Biocompatible End-to-End Fabrication of Cell-Laden Microgels.

Droplet microfluidics are extensively utilized to generate monodisperse cell-laden microgels in biomedical applications. However, maintaining cell viability is still challenging due to overexposure to harsh conditions in subsequent procedures that recover the microgels from the oil phase. Here, a gravity-oriented microfluidic device for end-to-end fabrication of cell-laden microgels is reported, which integrates dispersion, gelation, and extraction into a continuous workflow. This innovative on-chip extraction, driven by native buoyancy and kinetically facilitated by pseudosurfactant, exhibits 100% retrieval efficiency for microgels with a wide range of sizes and stiffnesses. The viability of encapsulated cells is perfectly maintained at ≈98% with minimal variations within and between batches. The end-to-end fabrication remarkably enhances the biocompatibility and practicality of microfluidics-based cell encapsulation and is promising to be compatible with various applications ranging from single-cell analysis to clinical therapy.

Cell-type-directed design of synthetic enhancers.

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Human venous blood derivatives as fetal bovine serum substitute for fibroblast culture cells in a fibrin construct

Aim: Venous blood derivatives (VBDs) have been suggested as substitutes for Fetal Bovine Serum (FBS) to improve the clinical transition of cell-based therapies. The literature is not clear about which is the best VBDs substitute. The present study aimed to evaluate the influence of VBDs on cell viability and describe a new method to seed these cells in a 3D Platelet-Rich Fibrin (PRF). Methods: Blood was processed to obtain Platelet-Poor Plasma from PRF (P-PRF), Human Serum (HS), Platelet-Poor Plasma from PRP (P-PRP), activated-PRP (a-PRP), and Platelet lysate (PL). Cells were supplemented with each VBD at 10% and FBS at 10% was the control. Cell viability (fibroblast 3T3/NIH) test was evaluated with MTT assay in two ways: i) cell-seeded and expanded with VBD; ii) cell-seed with FBS and expanded with VBD. To seed the Fibrin construct, cells were suspended in PBS and dropped into the blood sample before performing Choukroun's protocol for PRF. Constructs were cultured for 7 days in VBD supplements and FBS. Histological and Immunohistochemical analysis with vimentin was performed. Cell viability was analyzed by one-way ANOVA. Results: VBD's production time was very heterogeneous. Cells expanded in HS and a-PRP has grown faster. VBD-supplemented culture media provided cell culture highly sensible to trypsin/EDTA 0.25%. Cells seeded and expanded with VBD presented viability comparable to FBS in HS, a-PRP, and P-PRP (p>0.05) and lower in P-PRF and PL groups (p<0.05). The viability of cell seed with FBS and expanded with VBD was similar between P-PRF, a-PRP, PL, and FBS (p>0.05) and lower in HS and P-PRP (p<0.005). PRF-seeded cells showed a positive expression of vimentin and were able to maintain all cells supplemented with VBD. Conclusion: VBD supplements were able to maintain fibroblast cells in 2D and 3D cultures. The new method of the fibrin-cell construct was efficient to insert the cells into the fibrin network

The free energy landscape of small-world networks of cells.

The properties of organs, tissues, organoids, and other systems of cells, are influenced by the spatial localization and distribution of their elements. Here, we used networks to describe distributions of cells on a surface where the small-world coefficient (SW) of the networks was varied between SW~1 (random uniform distributions) and SW~10 (clustered distributions). The small-world coefficient is a topological measure of graphs: networks with SW>1 are topologically biased to transmit information. For each system configuration, we then determined the total energy U as the sum of the energies that describe cell-cell interactions - approximated by a harmonic potential. The graph of energy (U) across the configuration space of the networks (SW) is the energy landscape: it indicates which configuration a system of cells will likely assume over time. We found that, depending on the model parameters, the energy landscapes of 2D distributions of cells may be of different types: from type I to type IV. Type I and type II systems have high probability to evolve into random distributions. Type III and type IV systems have a higher probability to form clustered architectures. A great many of simulations indicated that cultures of cells with high initial density and limited sensing range could evolve into clustered configurations with enhanced topological characteristics. Moreover, the strongest the binding between cells, the greater the likelihood that they will assume configurations characterized by finite values of SW. Results of the work are relevant for those working the field of tissue engineering, regenerative medicine, the formation of in-vitro-models, the analysis of neuro-degenerative diseases.

nteraction between FAK/αB-crystalline is important for viability of the glioblastoma cells

Abstract Glioblastoma multiforme is a tumor of the central nervous system. Focal Adhesion Kinase (FAK) and αB-crystalline are two proteins involved in glioblastoma development. In this study, we investigated whether the FAK/αB-crystalline interaction is important for glioblastoma cells, we aimed to investigate the interaction of these two proteins in the glioblastoma multiforme cell line U87-MG. Two peptides named FP01 peptide (derived from αB-crystalline) and FP02 peptide (derived from FAK) were synthesized for this study. Treatment of U87-MG with the peptides FP01 and FP02 in the concentration at 50 µM reduced the viability cellular to around 41% and 51%, respectively. Morphological alterations in the cells treated with the peptides when compared to the control were observed. This study suggests that the interaction between FAK and αB-crystalline is important for the viability of glioblastoma cells

The regulatory effect of growth differentiation factor 11 on different cells.

Growth differentiation factor 11 (GDF11) is one of the important factors in the pathophysiological process of animals. It is widely expressed in many tissues and organs of animals, showing its wide biological activity and potential application value. Previous research has demonstrated that GDF11 has a therapeutic effect on various diseases, such as anti-myocardial aging and anti-tumor. This has not only sparked intense interest and enthusiasm among academics but also spurred some for-profit businesses to attempt to develop GDF11 as a medication for regenerative medicine or anti-aging application. Currently, Sotatercept, a GDF11 antibody drug, is in the marketing application stage, and HS-235 and rGDF11 are in the preclinical research stage. Therefore, we believe that figuring out which cells GDF11 acts on and its current problems should be an important issue in the scientific and commercial communities. Only through extensive, comprehensive research and discussion can we better understand the role and potential of GDF11, while avoiding unnecessary risks and misinformation. In this review, we aimed to summarize the role of GDF11 in different cells and its current controversies and challenges, providing an important reference for us to deeply understand the function of GDF11 and formulate more effective treatment strategies in the future.

What is a cell type?

A next step for cell atlases should be to chart perturbations in human model systems.

Bye bye, linearity, bye: quantification of the mean for linear CRNs in a random environment.

Molecular reactions within a cell are inherently stochastic, and cells often differ in morphological properties or interact with a heterogeneous environment. Consequently, cell populations exhibit heterogeneity both due to these intrinsic and extrinsic causes. Although state-of-the-art studies that focus on dissecting this heterogeneity use single-cell measurements, the bulk data that shows only the mean expression levels is still in routine use. The fingerprint of the heterogeneity is present also in bulk data, despite being hidden from direct measurement. In particular, this heterogeneity can affect the mean expression levels via bimolecular interactions with low-abundant environment species. We make this statement rigorous for the class of linear reaction systems that are embedded in a discrete state Markov environment. The analytic expression that we provide for the stationary mean depends on the reaction rate constants of the linear subsystem, as well as the generator and stationary distribution of the Markov environment. We demonstrate the effect of the environment on the stationary mean. Namely, we show how the heterogeneous case deviates from the quasi-steady state (Q.SS) case when the embedded system is fast compared to the environment.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy.

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.