Leveraging interindividual variability in threat conditioning of inbred mice to model trait anxiety.

Trait anxiety is a major risk factor for stress-induced and anxiety disorders in humans. However, animal models accounting for the interindividual variability in stress vulnerability are largely lacking. Moreover, the pervasive bias of using mostly male animals in preclinical studies poorly reflects the increased prevalence of psychiatric disorders in women. Using the threat imminence continuum theory, we designed and validated an auditory aversive conditioning-based pipeline in both female and male mice. We operationalised trait anxiety by harnessing the naturally occurring variability of defensive freezing responses combined with a model-based clustering strategy. While sustained freezing during prolonged retrieval sessions was identified as an anxiety-endophenotype behavioral marker in both sexes, females were consistently associated with an increased freezing response. RNA-sequencing of CeA, BLA, ACC, and BNST revealed massive differences in phasic and sustained responders' transcriptomes, correlating with transcriptomic signatures of psychiatric disorders, particularly post-traumatic stress disorder (PTSD). Moreover, we detected significant alterations in the excitation/inhibition balance of principal neurons in the lateral amygdala. These findings provide compelling evidence that trait anxiety in inbred mice can be leveraged to develop translationally relevant preclinical models to investigate mechanisms of stress susceptibility in a sex-specific manner.

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

SUMMARY: Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps. AVAILABILITY AND IMPLEMENTATION: ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/.

A Call for Gene Expression Analysis in Whole Blood of Patients With Rheumatoid Arthritis (RA) as a Biomarker for RA-Associated Interstitial Lung Disease.

OBJECTIVE: Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is one of the most common and prognostic organ manifestations of RA. Therefore, to allow effective treatment, it is of crucial importance to diagnose RA-ILD at the earliest possible stage. So far, the gold standard of early detection has been high-resolution computed tomography (HRCT) of the lungs. This procedure involves considerable radiation exposure for the patient and is therefore unsuitable as a routine screening measure for ethical reasons. Here, we propose the analysis of characteristic gene expression patterns as a biomarker to aid in the early detection and initiation of appropriate, possibly antifibrotic, therapy. METHODS: To investigate unique molecular patterns of RA-ILD, whole blood samples were taken from 12 female patients with RA-ILD (n = 7) or RA (n = 5). The RNA was extracted, sequenced by RNA-Seq, and analyzed for characteristic differences in the gene expression patterns between patients with RA-ILD and those with RA without ILD. RESULTS: The differential gene expression analysis revealed 9 significantly upregulated genes in RA-ILD compared to RA without ILD: arginase 1 (ARG1), thymidylate synthetase (TYMS), sortilin 1 (SORT1), marker of proliferation Ki-67 (MKI67), olfactomedin 4 (OLFM4), baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5), membrane spanning 4-domains A4A (MS4A4A), C-type lectin domain family 12 member A (CLEC12A), and the long intergenic nonprotein coding RNA (LINC02967). CONCLUSION: All gene products of these genes (except for LINC02967) are known from the literature to be involved in the pathogenesis of fibrosis. Further, for some, a contribution to the development of pulmonary fibrosis has even been demonstrated in experimental studies. Therefore, the results presented here provide an encouraging perspective for using specific gene expression patterns as biomarkers for the early detection and differential diagnosis of RA-ILD as a routine screening test.