LCM-seq identifies robust markers of vulnerable and resistant human midbrain dopamine neurons

Abstract

Defining transcriptional profiles of substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) dopamine neurons in human is critical to understanding their differential vulnerability in Parkinson Disease. However, reported marker profiles for these neuron populations are derived predominantly from rodents, utilize small sample sizes and display extensive variability between studies. Here, we map selective expression profiles of dopamine neurons in an extensive collection of human SNc and VTA using laser capture microdissection coupled with Smart-seq2 RNA sequencing (LCM-seq). By applying a bootstrapping strategy as sample input to DESeq2, we identify 33 differentially expressed SNc- or VTA-specific markers and we also compute the minimal cohort size required to identify differentially expressed genes (DEGs) that are concordant regardless of cohort size. Among the identified DEGs, ZCCHC12, CDH13 and SERPINE2, are minimally required to distinguish SNc or VTA dopamine neurons in both human and mouse. In summary, our study identifies novel markers, besides previously identified ones, which will be instrumentsal for future studies aiming to modulate dopamine neuron resilience as well as validate cell identity of stem cell-derived dopamine neurons.

Single cell analysis of gene expression in the substantia nigra pars compacta of a pesticide-induced mouse model of Parkinson’s disease

“…The network was significantly enriched in terms related to collagen, extracellular matrix and basement membrane (FDR < 8.49 ´ 10 -4 ) (Figure 5A). Other studies have also implicated changes in the extracellular matrix as a result of PD (Aguila et al, 2021;Freitas et al, 2021;Sandor et al, 2017). One hub gene, Cacna1c, had 31 interactions (Figure 5B).…”

A biophysical regulator of inhibitory integration and learning in mesolimbic dopamine neurons

“…Our findings may also be relevant for the understanding of human disease. A recent study into the transcriptomes of human midbrain DA neurons found that KCNIP4 expression is a key molecular signature of VTA DA neurons (as opposed to SN DA neurons) and is also strongly associated with DA neuron survival in Parkinson's disease (Aguila et al, 2021). While this study did not differentiate between different KCNIP4 isoforms, it could be that the differential expression of KChIP4a plays an important role in determining DA neuron vulnerability in Parkinson's disease, especially since Kv4 current reductions in SN DA neurons have been observed in experimental models of this illness (Subramaniam et al, 2014).…”

Deciphering the dynamic niches and regeneration-associated transcriptional program of motoneurons following peripheral nerve injury

“…Our estimation is approximate to the fold-change (134-fold) of Rpl22 HA immunoprecipitation (IP) in ipsilateral motoneurons compared to the contralateral ones from RiboTag; ChAT Cre mouse model at 7 days post-SNC (Shadrach et al, 2021), suggesting high reproducibility and accuracy of our data. Besides, we employed bootstrapping strategy to identify robust differential expression genes (DEGs) (Aguila et al, 2021) and showed remarkably robust (R90%) differential expression of Atf3 in state 2 and state 3 (Figure S5). The SCENIC (Aibar et al, 2017) analysis showed that the activity of Atf3 regulon was also significantly higher in state 2 and state 3, indicating Atf3 was mostly activated as early as 1 day (Student's t test, *p < 0.05, **p < 0.0001, Figure S5).…”

“…Moreover, captured and discriminated ''intact'' motoneurons and its minimal niches at 1, 3, and 7 days provide a valuable control for further screening crucial molecules involved in axon regeneration. We next employed bootstrap strategy (Aguila et al, 2021) to identify robust and reliable DEGs (average frequency R50%). 80 robust DEGs presented in early injury (61 up-and 19 downregulated genes, 0.5-12 h, Figure 3B and Table S3).…”

“…Laser capture microscopy (LCM) combined with antibody rapid staining could capture cell types of interest in situ and coupled with Smart-seq2 (LCM-seq) could provide the accurate quantification of gene expression in dissected cells (Nichterwitz et al, 2016). LCM-seq has been successfully applied in studies of nervous system diseases, including Parkinson disease and amyotrophic lateral sclerosis (ALS) (Aguila et al, 2021;Nichterwitz et al, 2016Nichterwitz et al, , 2020. Unlike other cell isolation methods, such as fluorescence-activated cell sorting and microfluidics, LCM could harvest target cells from the microenvironment without dissociation of the tissue and preserved in situ spatial information (Nichterwitz et al, 2016).…”