Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, “success”- and “failure” related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial’s outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

Cell-type-selective expression of the TFIID subunit TAF(II)105 (renamed TAF4b) in the ovary is essential for proper follicle development. Although a multitude of signaling pathways required for folliculogenesis have been identified, downstream transcriptional integrators of these signals remain largely unknown. Here, we show that TAF4b controls the granulosa-cell-specific expression of the proto-oncogene c-jun, and together they regulate transcription of ovary-selective promoters. Instead of using cell-type-specific activators, our findings suggest that the coactivator TAF4b regulates the expression of tissue-specific genes, at least in part, through the cell-type-specific induction of c-jun, a ubiquitous activator. Importantly, the loss of TAF4b in ovarian granulosa cells disrupts cellular morphologies and interactions during follicle growth that likely contribute to the infertility observed in TAF4b-null female mice. These data highlight a mechanism for potentiating tissue-selective functions of the basal transcription machinery and reveal intricate networks of gene expression that orchestrate ovarian-specific functions and cell morphology.

The study of synaptic specificity and plasticity in the CNS is limited by the inability to efficiently visualize synapses in identified neurons using light microscopy. Here, we describe synaptic tagging with recombination (STaR), a method for labeling endogenous presynaptic and postsynaptic proteins in a cell-type-specific fashion. We modified genomic loci encoding synaptic proteins within bacterial artificial chromosomes such that these proteins, expressed at endogenous levels and with normal spatiotemporal patterns, were labeled in an inducible fashion in specific neurons through targeted expression of site-specific recombinases. Within the Drosophila visual system, the number and distribution of synapses correlate with electron microscopy studies. Using two different recombination systems, presynaptic and postsynaptic specializations of synaptic pairs can be colabeled. STaR also allows synapses within the CNS to be studied in live animals noninvasively. In principle, STaR can be adapted to the mammalian nervous system.

Neocortical spiking dynamics control aspects of behavior, yet how these dynamics emerge during motor learning remains elusive. Activity-dependent synaptic plasticity is likely a key mechanism, as it reconfigures network architectures that govern neural dynamics. Here, we examined how the mouse premotor cortex acquires its well-characterized neural dynamics that control movement timing, specifically lick timing. To probe the role of synaptic plasticity, we have genetically manipulated proteins essential for major forms of synaptic plasticity, Ca2+/calmodulin-dependent protein kinase II (CaMKII) and Cofilin, in a region and cell-type-specific manner. Transient inactivation of CaMKII in the premotor cortex blocked learning of new lick timing without affecting the execution of learned action or ongoing spiking activity. Furthermore, among the major glutamatergic neurons in the premotor cortex, CaMKII and Cofilin activity in pyramidal tract (PT) neurons, but not intratelencephalic (IT) neurons, is necessary for learning. High-density electrophysiology in the premotor cortex uncovered that neural dynamics anticipating licks are progressively shaped during learning, which explains the change in lick timing. Such reconfiguration in behaviorally relevant dynamics is impeded by CaMKII manipulation in PT neurons. Altogether, the activity of plasticity-related proteins in PT neurons plays a central role in sculpting neocortical dynamics to learn new behavior.

Mutations in methyl-CpG-binding protein 2 (MeCP2) cause Rett syndrome and related autism spectrum disorders (Amir et al., 1999). MeCP2 is believed to be required for proper regulation of brain gene expression, but prior microarray studies in Mecp2 knock-out mice using brain tissue homogenates have revealed only subtle changes in gene expression (Tudor et al., 2002; Nuber et al., 2005; Jordan et al., 2007; Chahrour et al., 2008). Here, by profiling discrete subtypes of neurons we uncovered more dramatic effects of MeCP2 on gene expression, overcoming the "dilution problem" associated with assaying homogenates of complex tissues. The results reveal misregulation of genes involved in neuronal connectivity and communication. Importantly, genes upregulated following loss of MeCP2 are biased toward longer genes but this is not true for downregulated genes, suggesting MeCP2 may selectively repress long genes. Because genes involved in neuronal connectivity and communication, such as cell adhesion and cell-cell signaling genes, are enriched among longer genes, their misregulation following loss of MeCP2 suggests a possible etiology for altered circuit function in Rett syndrome.

Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500-1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100-200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

Generalist methods for cellular segmentation have good out-of-the-box performance on a variety of image types. However, existing methods struggle for images that are degraded by noise, blurred or undersampled, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases, and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry or undersampled images. Unlike previous approaches, which train models to restore pixel values, we trained Cellpose3 to output images that are well-segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as “one-click” buttons inside the graphical interface of Cellpose as well as in the Cellpose API.

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation algorithm called Cellpose, which can very precisely segment a wide range of image types out-of-the-box and does not require model retraining or parameter adjustments. We trained Cellpose on a new dataset of highly-varied images of cells, containing over 70,000 segmented objects. To support community contributions to the training data, we developed software for manual labelling and for curation of the automated results, with optional direct upload to our data repository. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.