Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

Direct reprogramming based on genetic factors resembles a promising strategy to replace lost cells in degenerative diseases such as Parkinson's disease. For this, we developed a knock‐in mouse line carrying a dual dCas9 transactivator system (dCAM) allowing the conditional in vivo activation of endogenous genes. To enable a translational application, we additionally established an AAV‐based strategy carrying intein‐split‐dCas9 in combination with activators (AAV‐dCAS). Both approaches were successful in reprogramming striatal astrocytes into induced GABAergic neurons confirmed by single‐cell transcriptome analysis of reprogrammed neurons in vivo . These GABAergic neurons functionally integrate into striatal circuits, alleviating voluntary motor behavior aspects in a 6‐OHDA Parkinson's disease model. Our results suggest a novel intervention strategy beyond the restoration of dopamine levels. Thus, the AAV‐dCAS approach might enable an alternative route for clinical therapies of Parkinson's disease.

Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury.

Background: Primary aldosteronism is frequently caused by an adrenocortical aldosterone-producing adenoma (APA) carrying a somatic mutation that drives aldosterone overproduction. APAs with a mutation in KCNJ5 (APA- KCNJ5 ^MUT ) are characterized by heterogeneous CYP11B2 (aldosterone synthase) expression, a particular cellular composition and larger tumor diameter than those with wild-type KCNJ5 (APA- KCNJ5 ^WT ). We exploited these differences to decipher the roles of transcriptome and metabolome reprogramming in tumor pathogenesis. Methods: Consecutive adrenal cryosections (7 APAs and 7 paired adjacent adrenal cortex) were analyzed by spatial transcriptomics (10x Genomics platform) and metabolomics (in situ matrix-assisted laser desorption/ionization mass spectrometry imaging) co-integrated with CYP11B2 immunohistochemistry. Results: We identified intratumoral transcriptional heterogeneity that delineated functionally distinct biological pathways. Common transcriptomic signatures were established across all APA specimens which encompassed 2 distinct transcriptional profiles in CYP11B2-immunopositive regions ( CYP11B2 -type 1 or 2). The CYP11B2 -type 1 signature was characterized by zona glomerulosa gene markers and was detected in both APA- KCNJ5 ^MUT and APA- KCNJ5 ^WT . The CYP11B2 -type 2 signature displayed markers of the zona fasciculata or reticularis and predominated in APA- KCNJ5 ^MUT . Metabolites that promote oxidative stress and cell death accumulated in APA- KCNJ5 ^WT . In contrast, antioxidant metabolites were abundant in APA- KCNJ5 ^MUT . Finally, APA-like cell subpopulations—negative for CYP11B2 gene expression—were identified in adrenocortical tissue adjacent to APAs suggesting the existence of tumor precursor states. Conclusions: Our findings provide insight into intra- and intertumoral transcriptional heterogeneity and support a role for prooxidant versus antioxidant systems in APA pathogenesis highlighting genotype-dependent capacities for tumor expansion.