The Human Neural Organoid Cell Atlas: An atlas of 3 million cells spanning the gastrointestinal tract and the gastrointestinal tract
Various metaphors have been used to describe the atlas: some have called it the ‘Google Maps of the human body’, others ‘the periodic table of cells’. It is an open source encyclopedia and a bottom-up resource for cells, established and run by the scientific community, and researchers intend it to remain open access in the long term. Funding will continue to be essential.
The gastrointestinal tract atlas by Oliver et al.2 spans from the tissue of the mouth through to the oesophagus, the stomach, intestines and the colon. Many previous data sets have been created, but the present atlas deftly integrates these into a large-scale atlas of 1.1 million cells, with annotations of the resident cell types and states. The authors have data sets for people who have inflammatory diseases such as coeliac disease, and for those with Crohn’s disease. Intestinal inflammation can cause cells to undergo metaplasia, a shift from one cell type to another. By analysing the layered data sets, the authors inferred the origin of these metaplastic cells by comparing them with stem cells in the atlas. This insight highlights the benefit of the atlas’s completeness, which allows for the comparison of disease states in one organ with the normal states of cells in different organs.
Although the brain is intensely studied, organoids have become a powerfully tractable model for functional analysis. The human neural organoid cell atlas described by He et al.3 is built on 1.7 million cells by integrating 36 single-cell RNA-sequencing data sets, generated with 26 protocols for producing organoids from cultured cells. The atlas is already shedding light on the crucial question of how faithfully organoids capture aspects of the developing brain. The authors found that the organoid resembles the cellular state of the fetal brain in the first 3 months of culture, but it doesn’t do that for the next 3 months. But the authors found an intriguing limit to this correspondence. The diversification in neuronal cell types that occurs with development did not continue in the organoid, and the fetal brain during the last trimester of pregnancy was not captured — leaving an open question about which required signals or other features are missing from organoid models.
Yayon et al.4 created a map of the thymus, a lymphoid organ that produces immune cells, in its early fetal development and early postnatal stages. Using the spatial method, the authors created a framework to map the tissue. This model of the axis between the outer part of the thymus and its centre (the cortico-medullary axis) allows for a deeper understanding of tissue organization and comparison of the organ both in and between individuals. It might be interesting to look at how this is applicable to other parts of life, such as old age.
embryotic and fetal development causes the self organization of cells over time and space which leads to the creation of tissues and the establishment of body functions. Owing to the immense complexity of these processes, scientists’ understanding of the molecular and cellular mechanisms that underlie such developmental events, particularly in humans, remains limited.
To and colleagues explored the embryonic and fetal development of part of the skull and joints of the limbs 5–11 weeks after conception. Through the simultaneous mapping of transcriptomic and epigenomic profiles of single cells, they identified key gene-regulatory networks that direct the commitment of cells to chondrogenic (cartilage-forming) and osteogenic (bone-forming) lineages. The authors propose how cellular crosstalk might guide the formation of bone and how it could interact with thevascular system. The authors took the data derived from genome-wide association studies and combined them with their single-cell analysis to identify cell states that are potentially linked to complex features of the adult skeleton, such as arthritis.
Similarly, Gopee and colleagues present a comprehensive cellular atlas of skin development spanning 7–17 weeks after conception. Using a combination of single-cell and spatial transcriptomic technologies, the authors mapped dynamic changes in cell states and detail how these cells organize to form developmental structures and interact in microanatomical skin niches. The role of immune cells in coordinating development and forming blood vessels is spotlighted in their findings. The organoid system recapitulates key aspects of skin development.
scTab is a deep-learning model built on single-cellRNA-sequencing data that is tailored to annotating cell types in different tissues. Recognizing the limitations of conventional machine learning in handling large, diverse data sets, the developers of scTab introduce a data-augmentation approach for single-cell sequencing data to increase the size of the training data set, enabling generalizations to be made across tissues. Fischer et al. demonstrate that complex nonlinear models outperform simpler linear ones in cell-type classification, especially when trained on extensive data sets. scTab has the potential to promote standardized cell labels and encourage a consensus in cell-type terminology.
MultiDGD, described by Schuster et al.20, tackles integrating multimodal data, such as gene expression and the accessibility of chromatin (the packaged form of DNA) to transcriptional machinery, using a ‘deep latent variable’ model. MultiDGD learns the most optimal hidden-variable representations that are shared across the data, without the need to define important features. MultiDGD can be used for multi-omics studies in which data were gathered from different sources, as it incorporates information about potentially confoundable variables such as inconsistencies between samples. This model’s clustering of shared representations improves the alignment of multimodal data, enabling associations between genes and regulatory regions of the genome to be mapped — an essential step in understanding gene-regulatory networks.
PopV and scTab lead the efforts in standardized annotation and consensus-building, whereas multiDGD opens up avenues for data integration across complex multimodal data sets.
This does not lessen the impact of these methods, but rather highlights the field’s rapid pace and the importance of innovation. To meet this growth, future research is likely to emphasize adaptable and interoperable solutions. These methods contribute valuable foundations for future advancements, paving the way for even more adaptable and scalable models for single-cell, multi-omics data.
The Human Cell Atlas: How Many Years Have You Done? Evidence from a Global Collaboration of Scientists and Funders — An Updated Look
The studies emerging from the global project are a major achievement just ten years after its launch. To sign up for the long haul, funders should do so.
Findings from researchers working on the lung cell atlas, for example, highlight the differences between the lungs of a sample of people in Malawi who died from COVID-19 and those who died from other lung diseases2. Scientists have also been studying the development of organs during gestation, for example, through analyses of prenatal human skin3 and developing joints and craniums4.
The HCA could not have been achieved without earlier projects like the Human Genome Project as well as the NIH BRAIN Initiative. The HCA teams have also worked hard to reflect human diversity in their data. The scientists from Africa, Asia, Latin America and the Middle East are part of the consortium. Researchers from these regions were invited not only to join, but also to help lead and coordinate HCA projects, and to do so according to priorities relevant to local populations. More than 3,000 scientists are involved in the initiative, and they are studying data from around the world.
Most research projects have a limited lifespan. It is considered generous if you have been there ten years. A handful of projects might last a few years longer. Critical infrastructure projects such as essential infrastructure are usually reserved for permanent funding because they don’t have the tools and technologies to make discoveries and inventions. That is what the HCA needs to be compared to.
Unveiling Active Cells in the Human Genome: How Do Genetic Studies Can Help to Understand and Identify Disease-Associated Variables?
HCA researchers said that genetic studies have mapped more than 100,000 disease-associated variants in the human genome but they don’t know which cells are active. Without this knowledge, they add, “we cannot fully understand biology, study more powerful models of disease, deploy better diagnostics, and develop more effective therapies”.