@article{22295,
  abstract     = {Despite the functional diversity of over 100 causal genes1,2,3, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.},
  author       = {Schwarz, Lena A and Dotter, Christoph and Isaev, Sergey and Lisi, Michela and Malzl, Daniel and Büschl, Christoph and Ladstätter, Sabrina and Oliveira, Bárbara and Barel, Matteo and Basilico, Bernadette and Chintaluri, Chaitanya and Gorkiewicz, Sarah and Goudarzi, Mohammad and Belinova, Tereza and Reichl, Stephan and Sendžikaitė, Gintarė and Arcot Jayaram, Satish and Koppensteiner, Peter and Sommer, Christoph M and Vogels, Tim P and Menche, Jörg and Adameyko, Igor and Kharchenko, Peter Vasili and Bock, Christoph and Novarino, Gaia},
  issn         = {1476-4687},
  journal      = {Nature},
  publisher    = {Springer Nature},
  title        = {{Cortical development dynamics across autism spectrum disorder mouse models}},
  doi          = {10.1038/s41586-026-10679-1},
  year         = {2026},
}

@article{13033,
  abstract     = {Current methods for assessing cell proliferation in 3D scaffolds rely on changes in metabolic activity or total DNA, however, direct quantification of cell number in 3D scaffolds remains a challenge. To address this issue, we developed an unbiased stereology approach that uses systematic-random sampling and thin focal-plane optical sectioning of the scaffolds followed by estimation of total cell number (StereoCount). This approach was validated against an indirect method for measuring the total DNA (DNA content); and the Bürker counting chamber, the current reference method for quantifying cell number. We assessed the total cell number for cell seeding density (cells per unit volume) across four values and compared the methods in terms of accuracy, ease-of-use and time demands. The accuracy of StereoCount markedly outperformed the DNA content for cases with ~ 10,000 and ~ 125,000 cells/scaffold. For cases with ~ 250,000 and ~ 375,000 cells/scaffold both StereoCount and DNA content showed lower accuracy than the Bürker but did not differ from each other. In terms of ease-of-use, there was a strong advantage for the StereoCount due to output in terms of absolute cell numbers along with the possibility for an overview of cell distribution and future use of automation for high throughput analysis. Taking together, the StereoCount method is an efficient approach for direct cell quantification in 3D collagen scaffolds. Its major benefit is that automated StereoCount could accelerate research using 3D scaffolds focused on drug discovery for a wide variety of human diseases.},
  author       = {Zavadakova, Anna and Vistejnova, Lucie and Belinova, Tereza and Tichanek, Filip and Bilikova, Dagmar and Mouton, Peter R.},
  issn         = {2045-2322},
  journal      = {Scientific Reports},
  keywords     = {Multidisciplinary},
  number       = {1},
  publisher    = {Springer Nature},
  title        = {{Novel stereological method for estimation of cell counts in 3D collagen scaffolds}},
  doi          = {10.1038/s41598-023-35162-z},
  volume       = {13},
  year         = {2023},
}