Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.

Direct lineage reprogramming of glial cells to induced neurons has the potential for restoring brain circuits and function in neuronal disorders and states. We introduce three-dimensional (3D) human glia reprogramming into neurons with a GABAergic interneuron phenotype using stem cell–derived human glia. Single-nucleus RNA sequencing of the converted cells demonstrates distinct neuronal clusters within 2 weeks, including a parvalbumin (PV) cluster with high neuronal maturity and features of chandelier interneurons. A lineage trajectory analysis of the glia-to-neuron conversion reveals a distinct lineage pathway to PV chandelier fate, including various neuronal developmental stages and the establishment of synaptic machinery. This analysis reveals PV fate-important genes that are previously unknown to neural reprogramming with promising functional importance for future derivations. Our data demonstrate successful human glia conversion into interneurons with features of bona fide PV subtype and highlight the reprogramming trajectory with key transitional genes. This advancement holds promise for future human brain cell engineering and repair.

Summary: Parvalbumin (PV) interneurons are crucial for synaptic plasticity, and their damage or loss is linked to various neurological disorders. Yet, generating these cells of human source in vitro is challenging, limiting advancements in cell repair and disease modelling. We introduce a novel approach to derive human PV neurons through direct reprogramming of glial precursor cells (GPCs). Using ectopic expression of GABAergic neuronal genes, GPCs efficiently convert into GABAergic interneurons in 3D culture environment within weeks and achieve functional neuronal maturity. Single-nuclei RNA sequencing identified a distinct PV neuronal cluster with high maturity and characteristics of PV chandelier subclass that are equivalent to bona fide human interneurons. Trajectory analysis revealed a distinct glia-to-PV interneuron conversion pathway, involving several new transitory genes, with potential for functional importance for PV derivation. Our data introduces a new strategy for generating human PV interneurons, promising significant implications for future generation of patient-specific PV neurons both in vitro and in vivo . Highlights: A novel approach to derive human PV interneurons by direct glia reprogramming. First comprehensive transcriptomic profiling of induced human PV interneurons. Induced PV interneurons are of chandelier subtype with transcriptional similarity to bona fide interneurons. Successful glia-to-PV interneuron conversion passes through a specific reprogramming trajectory and involves key genes with functional potential.

In the dawning era of artificial intelligence (AI), health care stands to undergo a significant transformation with the increasing digitalization of patient data. Digital imaging, in particular, will serve as an important platform for AI to be implemented to aid decision making and diagnostics. A growing number of studies demonstrate the potential of AI for automatic pre-surgical skin tumor delineation, which could have tremendous impact on clinical practice. However, current methods have the drawback of relying on a ground truth image in which the tumor borders are already identified, which is not clinically possible. We report a novel approach where hyperspectral images provides spectra from small regions representing healthy tissue and tumor, which are used to generate prediction maps using artificial neural networks. Thereafter, a segmentation algorithm automatically manages to determine the skin tumor borders. Our approach therefore circumvents the need for a complete ground truth image, where the training data is contained within each individual patient. This links to an important strength of our approach as we develop individual network models for each patient. Our approach is therefore not only more clinically relevant, but it also interesting for emerging precision skin tumor diagnostics where adaptability toward the individual is key.

T-cell development provides an excellent model system for studying lineage commitment from a multipotent progenitor. The intrathymic development process has been thoroughly studied. The molecular circuitry controlling it has been dissected and the necessary steps like programmed shut off of progenitor genes and T-cell genes upregulation have been revealed. However, the exact timing between decision-making and commitment stage remains unexplored. To this end, we implemented an agent-based multi-scale model to investigate inheritance in early T-cell development. Treating each cell as an agent provides a powerful tool as it tracks each individual cell of a simulated T-cell colony, enabling the construction of lineage trees. Based on the lineage trees, we introduce the concept of the last common ancestors (LCA) of committed cells and analyse their relations, both at single-cell level and population level. In addition to simulating wild-type development, we also conduct knockdown analysis. Our simulations predicted that the commitment is a three-step process that occurs on average over several cell generations once a cell is first prepared by a transcriptional switch. This is followed by the loss of the Bcl11b-opposing function approximately two to three generations later. This is when our LCA analysis indicates that the decision to commit is taken even though in general another one to two generations elapse before the cell actually becomes committed by transitioning to the DN2b state. Our results showed that there is decision inheritance in the commitment mechanism.

T-cell development provides an excellent model system for studying lineage commitment from a multipotent progenitor. The intrathymic development process has been thoroughly studied. The molecular circuitry controlling it has been dissected and the necessary steps like programmed shut off of progenitor genes and T-cell genes upregulation have been revealed. However, the exact timing between decision-making and commitment stage remains unexplored. To this end, we implemented an agent-based multi-scale model to investigate inheritance in early T-cell development. Treating each cell as an agent provides a powerful tool as it tracks each individual cell of a simulated T-cell colony, enabling the construction of lineage trees. Based on the lineage trees, we introduce the concept of the last common ancestors (LCA) of committed cells and analyse their relations, both at single-cell level and population level. In addition to simulating wild-type development, we also conduct knockdown analysis. Our simulations showed that the commitment is a three-step process over several cell generations where a cell is first prepared by a transcriptional switch. This is followed by the loss of the Bcl11b-opposing function two to three generations later which is when the decision to commit is taken. Finally, after another one to two generations, the cell becomes committed by transitioning to the DN2b state. Our results showed that there is inheritance in the commitment mechanism.