Single-cell analysis of prenatal and postnatal human cortical development

Velmeshev, Dmitry; Perez, Yonatan; Yan, Zihan; Valencia, Jonathan E.; Castañeda-Castellanos, David R.; Schirmer, Lucas; Mayer, Simone; Wick, Brittney; Wang, Shaohui; Nowakowski, Tomasz J.; Paredes, Mercedes F.; Huang, Eric J.; Kriegstein, Arnold R.

doi:10.1101/2022.10.24.513555

Cited by 22 publications

(29 citation statements)

References 43 publications

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“… 16 , 17 , 26 , 54 , 99 , 100 , 101 , 102 , 103 , 129 , 130 Notably, a recent genetic study of autistic patients suggested that, during development, autism-associated genes are strongly enriched in maturing excitatory neurons, compared to immature progenitors. 131 Further, in human cortical organoids, mutations in autism-associated genes perturb the maturation of lower layer PNs. 120 However, how and when mutant autism associated genes affect embryonic circuit development and activity in vivo are not well understood.…”

Section: Discussionmentioning

confidence: 99%

Pyramidal neurons form active, transient, multilayered circuits perturbed by autism-associated mutations at the inception of neocortex

Munz¹,

Bharioke²,

Kosche³

et al. 2023

Cell

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Section: Discussionmentioning

confidence: 99%

Pyramidal neurons form active, transient, multilayered circuits perturbed by autism-associated mutations at the inception of neocortex

Munz¹,

Bharioke²,

Kosche³

et al. 2023

Cell

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“…This finding is notable across cell type annotation and gene expression reconstruction. Extending to novel datasets, we evaluated the supervised and SSL models on five datasets [49][50][51][52] published after the CELLxGENE 48 census of scTab (Methods). In this setting, self-supervised pretraining improves performance (see Figure 2c, Supp Figure 2), e.g., from [0.0877 ± 0.0215] to [0.1797 ± 0.0450] macro F1 for cell type prediction in the Great Apes study 52 .…”

Section: The Efficacy Of Ssl Depends On Its Contextmentioning

confidence: 99%

“…49 III: Single-cell analysis of prenatal and postnatal human cortical development. 50 IV: Circulating Immune cells after CV19 infection, vaccination, and HC. 51 V: Human: Great apes study 52 ) measuring the macro F1 Score of a random baseline, a Zero-Shot SSL model, the supervised model, and an SSL model.…”

Section: The Efficacy Of Ssl Depends On Its Contextmentioning

confidence: 99%

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Richter,

Bahrami,

Xia

et al. 2024

Preprint

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Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabeled datasets, already transforming computer vision and natural language processing. Similarly, in single-cell genomics (SCG), representation learning is well-recognized for offering insights into complex biological data, even more so by the advent of early foundation model approaches. However, despite these advancements, identifying scenarios in SCG where SSL outperforms traditional supervised or unsupervised learning methods remains a nuanced challenge. Furthermore, selecting the most effective pretext tasks within the SSL framework for SCG is a critical yet unresolved question. Here, we address this gap by adapting and benchmarking SSL techniques in SCG, including masked autoencoders with multiple masking strategies and contrastive learning approaches. Trained on over 20 million cells, this study rigorously examines multiple downstream tasks, including cell type prediction, gene expression reconstruction, cross-modality prediction, and data integration. Our empirical analyses underscore the nuanced role of SSL, namely in transfer learning scenarios leveraging auxiliary data or analyzing novel datasets. Masked autoencoders excel over contrastive methods in SCG, diverging from computer vision trends. Moreover, our findings reveal notable capabilities of SSL in zero-shot cell type prediction and offer insights into its potential benefits in cross-modality prediction and data integration. In summary, we study the application of SSL in SCG, minimizing model bias through simple, fully connected networks, and benchmark SSL's utility across key representation learning scenarios.

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“…Notably, many genes associated with such neurodevelopmental disorders (NDDs) are expressed early during brain development and are involved in gene regulation and synaptic function 3,4 . Studies using post-mortem human brain tissue provide evidence that cortical excitatory neurons are commonly dysregulated in autism 5,6 . However, since these are end of life studies, whether this a cause or consequence of autism is unclear.…”

Section: Introductionmentioning

confidence: 99%

MYT1L deficiency impairs excitatory neuron trajectory during cortical development

Yen,

Chen,

Skinner

et al. 2024

Preprint

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Mutations that reduce the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. Furthermore, MYT1L is routinely used as a proneural factor in fibroblast-to-neuron transdifferentiation. MYT1L has been hypothesized to play a role in the trajectory of neuronal specification and subtype specific maturation, but this hypothesis has not been directly tested, nor is it clear which neuron types are most impacted by MYT1L loss. In this study, we profiled 313,335 nuclei from the forebrains of wild-type and MYT1L-deficient mice at two developmental stages: E14 at the peak of neurogenesis and P21, when neurogenesis is complete, to examine the role of MYT1L levels in the trajectory of neuronal development. We found that MYT1L deficiency significantly disrupted the relative proportion of cortical excitatory neurons at E14 and P21. Significant changes in gene expression were largely concentrated in excitatory neurons, suggesting that transcriptional effects of MYT1L deficiency are largely due to disruption of neuronal maturation programs. Most effects on gene expression were cell autonomous and persistent through development. In addition, while MYT1L can both activate and repress gene expression, the repressive effects were most sensitive to haploinsufficiency, and thus more likely mediate MYT1L syndrome. These findings illuminate the intricate role of MYT1L in orchestrating gene expression dynamics during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome.

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Single-cell analysis of prenatal and postnatal human cortical development

Cited by 22 publications

References 43 publications

Pyramidal neurons form active, transient, multilayered circuits perturbed by autism-associated mutations at the inception of neocortex

Pyramidal neurons form active, transient, multilayered circuits perturbed by autism-associated mutations at the inception of neocortex

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

MYT1L deficiency impairs excitatory neuron trajectory during cortical development

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