Isabel Espinosa-Medina

Cell-state diversity drives tissue adaptability, repair, and disease resilience, but capturing this complexity is a challenge. Current approaches rely on transcriptional profiling and overlook organelle structure, a key indicator of metabolism and stress. We developed spatial Organellomics (sOrganellomics), an imaging workflow that integrates automated segmentation with machine learning to classify and spatially map cell states from multi-organelle signatures. In liver and pancreas, these signatures distinguished broad cellular classes. In liver, sOrganellomics revealed that zonal position did not fully explain organelle-defined hepatocyte categories. Instead, hepatocytes formed intermixed communities within canonical zones, supporting a refined subzonal diversity model. Nutritional stress reshaped this organization. Intravital imaging linked fasting-induced organelle remodeling with altered mitochondrial membrane potential in vivo, supporting multi-organelle architecture as a structural readout of tissue adaptation.

Islets of Langerhans constitute the endocrine component of the pancreas and comprise the main cell types responsible for the regulation of glucose homeostasis. Beta cells produce and secrete insulin, the principal hormone capable of lowering blood glucose. However, the interplay of beta cells with other endocrine cell types, such as alpha cells and delta cells, is critical for proper blood glucose regulation. The failure of beta cells to produce sufficient amounts of insulin leads to diabetes, a major metabolic disease. Since their discovery by Paul Langerhans using a simple light microscope in 1869, islets have been imaged using many different modalities. From the middle of the 20th century, electron microscopy has been a key tool in resolving the ultrastructure of islet cells, thereby making important contributions to the understanding of protein trafficking in general, and islet cell function as well as dysfunction in diabetes. Recent advances in microscopy techniques as well as the broader availability of analysis tools have elevated islet imaging from two dimensions into three dimensions and enable nanoscale imaging within the tissue context. Here, we review work on islet ultrastructure, from landmark studies published in the 1960s and 1970s to recent advances in imaging. Finally, we discuss open questions and propose ideas for integrating different modalities into structural imaging with the goal of better understanding islet function and dysfunction. Graphical Abstract

Does the stem cell origin impact on how daughter neurons acquire functional characteristics? Here, we uncovered that progenitor identity, defined by proneural gene expression, hardwires the functional mode (synchrony vs. asynchrony) of a neuron. Multimodal cell lineage reconstruction combining intersectional lineage tracing, newly developed computational tools, and genetic knockouts revealed that different progenitors produced functionally distinct neuronal subtypes in the zebrafish hindbrain. Dynamics of neuronal production suggest that progenitor competence changes over time. Whereas neurog1 -expressing progenitors contributed to both glutamatergic and GABAergic lineages at early embryonic stages, later, other progenitor pools also assumed this role. Whole-hindbrain 3D atlases, in which we combined calcium imaging to monitor spontaneous neuronal activity with genetic perturbations and progenitor origin, unveiled that the emergence of neuronal activity patterns was presaged by their progenitor identity. This suggests a link between cell ontogeny and neuronal activity in the zebrafish hindbrain, and more fundamentally how the embryonic progenitor identity controls the emergence of neuronal diversity in glutamatergic and GABAergic neuronal populations.

During development, cells undergo a sequence of specification events to form functional tissues and organs. To investigate complex tissue development, it is crucial to visualize how cell lineages emerge and to be able to manipulate regulatory factors with temporal control. We recently developed TEMPO (Temporal Encoding and Manipulation in a Predefined Order), a genetic tool to label with different colors and genetically manipulate consecutive cell generations in vertebrates. TEMPO relies on CRISPR to activate a cascade of fluorescent proteins which can be imaged in vivo. Here, we explain the steps to design, generate, and express TEMPO constructs in zebrafish and mice.

Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing this cellular complexity remains a challenge. Most current approaches rely on transcriptional profiling and often overlook functional insights embedded in organelle structure, key indicators of cellular metabolism and stress. Here, we introduce spatial Organellomics ( sOrganellomics ), an imaging workflow integrating automated segmentation with machine learning to classify and spatially map cell states based on multi-organelle features. Across metabolically specialized organs, these features distinguish organ-specific cellular identities. In the liver, sOrganellomics reveals a previously unrecognized hepatocyte organization: different hepatocyte states form intermixed communities rather than classical homogeneous graded zones, a pattern disrupted by nutritional stress. Intravital imaging further links fasting-associated architectural remodeling to altered mitochondrial membrane potential and bioenergetic heterogeneity in vivo . This approach maps metabolic transitions and early disease progression, establishing organelle architecture as a scalable readout of functional cell state diversity in tissues.

During development, regulatory factors appear in a precise order to determine cell fates over time. To investigate complex tissue development, one should not just label cell lineages but further visualize and manipulate cells with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labelling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation/inactivation of reporters/effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification. One-Sentence Summary Gaining sequential genetic access to vertebrate cell lineages.

Acquiring both lineage and cell-type information during brain development could elucidate transcriptional programs underling neuronal diversification. This is now feasible with single-cell RNA-seq combined with CRISPR-based lineage tracing, which generates genetic barcodes with cumulative CRISPR edits. This technique has not yet been optimized to deliver high-resolution lineage reconstruction of protracted lineages. Drosophila neuronal lineages are an ideal model to consider, as multiple lineages have been morphologically mapped at single-cell resolution. Here we find the parameter ranges required to encode a representative neuronal lineage emanating from 100 stem cell divisions. We derive the optimum editing rate to be inversely proportional to lineage depth, enabling encoding to persist across lineage progression. Further, we experimentally determine the editing rates of a Cas9-deaminase in cycling neural stem cells, finding near ideal rates to map elongated Drosophila neuronal lineages. Moreover, we propose and evaluate strategies to separate recurring cell-types for lineage reconstruction. Finally, we present a simple method to combine multiple experiments, which permits dense reconstruction of protracted cell lineages despite suboptimum lineage encoding and sparse cell sampling.

Summary: Developing cells divide and differentiate, and in many tissues, such as bone, muscle, and placenta, cells fuse acquiring specialized functions. While it is known that fused-cells are differentiated, it is unclear what mechanisms trigger the programmatic-change, and whether cell-fusion alone drives differentiation. To address this, we employed a fusogen-mediated cell-fusion system involving undifferentiated cells in tissue culture. RNA-seq analysis revealed cell-fusion initiates a dramatic transcriptional change towards differentiation. Dissecting the mechanisms causing this reprogramming, we observed that after cell-fusion plasma-membrane surface area decreases through increased endocytosis. Consequently, glucose-transporters are internalized, and cytoplasmic-glucose and ATP transiently decrease. This low-energetic state activates AMPK, which inhibits YAP1, causing cell-cycle arrest. Impairing either endocytosis or AMPK prevents YAP1 inhibition and cell-cycle arrest after fusion. Together these data suggest that cell-fusion-induced differentiation does not need to rely on extrinsic-cues; rather the plasma-membrane diminishment forced by the geometric-transformations of cell-fusion cause transient cell-starvation that induces differentiation.