Neurodegenerative diseases are characterized by a chronic and progressive structural and functional degeneration of cells in the central or peripheral nervous system, leading to massive neuronal loss. Unfortunately, there are currently no effective treatments for neurodegenerative diseases nor biomarkers for their early detection (Heemels, 2016). Data from the Global Burden of Diseases (GBD, 2020) demonstrates that Alzheimer’s and Parkinson’s diseases are the top neurodegenerative disorders contributing to the highest disability-adjusted life years (DALY) among the groups aged 75 or older (GBD 2019 Diseases and Injuries Collaborators, 2020). These common neurodegenerative diseases are predominantly idiopathic and have overlapping clinical and pathological features. Genome-wide association studies (GWAS) have identified genetic variants related to the risk of developing Alzheimer’s disease (AD) and Parkinson’s disease (PD). However, our understanding of the specific cell types dysregulated in these diseases remains elusive.

High throughput single-cell and single-nuclei mRNA sequencing (scRNA-seq/snRNA-seq) technologies have pioneered a new era of exploration into the diversity of brain cell types at the molecular level. Previously, the study of brain cells was limited to bulk assays in which the amount of RNA is averaged among all cells masking cell-type-specific gene expression (Colantuoni et al., 2011; Hawrylycz et al., 2012; Miller et al., 2014; Zhang et al., 2016). However, brain cells are heterogenous and functionally complex. ScRNA-seq/snRNA-seq technologies have provided an unprecedented opportunity to systematically investigate the expression of thousands of genes and cells, identifying cell subpopulations, and reconstructing temporal and spatial dynamics of gene expression. To this end, a comprehensive understanding of the brain cellular subtypes and their expression profiles in healthy physiological context and neurodegenerative conditions is required. Currently, scRNA-seq and snRNA-seq studies of mouse and human brains depict yields ranging from thousands of cells up to 1.3 million cells (Yao and van Velthoven, 2021). However, the scope of these studies can still be improved in the future given that the adult human brain has approximately 170 billion cells including a similar proportion of neuronal and non-neuronal cells (Azevedo et al., 2009). Exciting insights have been achieved with scRNA-seq and snRNA-seq in the field of human oligodendrocyte cell diversity (Marques et al., 2016), and functional states of human microglia (Masuda et al., 2019) among others. Furthermore, recent advances allow the correlation of molecular features with cellular processes such as proliferation leading to neurogenesis in the adult mouse hippocampus (Habib et al., 2016), as well as anatomical location and electrophysiological characteristics in the murine thalamus (Li et al., 2020).

Herein we will review the recent progress in understanding neurodegenerative diseases using single-cell sequencing. We will particularly focus on AD and PD. We will discuss the fundamental principles and implications of scRNA-seq and snRNA-seq of the human brain. Furthermore, we will briefly describe some examples of the computational and analytical tools used to interpret the extensive amount of data generated from these assays. Also, we will focus on pioneering human transcriptomic studies which have addressed the cellular heterogeneity of the brain regions mainly affected in AD and PD. Finally, we will highlight challenges and limitations in the application of these technologies in the study of AD and PD.

The first step in performing scRNA-seq/snRNA-seq is to identify the brain region of interest. Human brain cells or cell nuclei may be isolated from several sources: fresh tissue, archived organs, or from cells differentiated in vitro (Figure 1A). Samples from fresh tissue are collected from resection surgeries, biopsies, or autopsies (Gradišnik et al., 2021), while samples from archived organs are obtained from postmortem frozen or formalin-fixed paraffin-embedded brain sections (Wang et al., 2013). If frozen postmortem brains are selected, the best option is to collect the transcripts to be sequenced from the nucleus (snRNA-seq) because freezing ruptures the plasma membrane, thus complicating the isolation of intact cells and therefore increasing the probability of capturing degraded transcripts from the cytoplasm (Krishnaswami et al., 2016; Slyper et al., 2020; Maitra et al., 2021). Similarly, it is highly recommended to perform snRNA-seq instead of scRNA-seq on samples obtained from formalin-fixed tissues since the fixation process often damages the integrity of various cellular structures, leading to the detection of heavily degraded cytoplasmic RNA (Esteve-Codina et al., 2017). Furthermore, very recent protocols have optimized the extraction of cell nuclei for sequencing from formalin-fixed tissues, including brain (Chung et al., 2022; Vallejo et al., 2022). An alternative sample source is in vitro differentiation of either embryonic stem cells (ESCs) or reprogrammed induced pluripotent stem cells (iPSCs), which are a reliable source of biological material since the processes of artificial induction to the desired cell lineage highly recapitulates the molecular processes that occur in the organism during the development and maturation of cells (Marei et al., 2017). Moreover, some studies have already compared through scRNA-seq the transcriptomes of differentiated cells (e.g., dopaminergic neurons) from iPSCs and ESCs to the transcriptomes of cells obtained from human brain tissue to assess the fidelity of in vitro-derived cells, observing that these cells retain the characteristics from their in vivo counterparts (La Manno et al., 2016; Fernandes et al., 2020). Additionaly, some protocols optimized for scRNA-seq/snRNA-seq of in vitro differentiated cells recommend collecting the cells of interest by FACS sorting to increase the reliability of the procedure (Cuomo et al., 2020).

As mentioned above, the selection of the sequencing method may depend on the conservation status of the sample, but also on the type of cells to be analyzed. Single-cell isolation from brain represents a challenge because neurons are very sensitive to enzymatic and mechanical dissociation methods. Brain samples can be meticulously collected, for example using laser microdissection, and then carefully processed with an enzymatic or mechanical digestion method to obtain a single-cell suspension. However, as mentioned, there are at least two challenges when performing brain cell dissociation. First, since neuronal axons, dendrites, and synapses form an intricate and highly connected network in the brain, their dissociation may affect cellular integrity and even modify transcriptional profiles by upregulation of stress-induced artifacts (Denisenko et al., 2020). Second, neuronal transcripts residing in distal cellular compartments will most likely be lost upon dissociation (Cajigas et al., 2012; Tushev et al., 2018). These issues may lead to an underrepresentation of neuronal populations with respect to glial cells, which are more resistant to dissociation procedures as has been observed in single-cell suspensions obtained from human brain cortex (Darmanis et al., 2015). Therefore, careful consideration must be taken when selecting a cell dissociation method. Isolating single nuclei instead of single cells is a better option to analyze tissues that cannot be easily dissociated. Thus, an advantage of snRNA-seq is that it can capture transcripts from cells that are more susceptible to death in the process of dissociation for example, neurons from the brain cortex (Tasic et al., 2018). Moreover, nuclei isolation protocols are fast and do not require protease digestion or heating, reducing the probability of aberrant transcription (Lacar et al., 2016); also, nuclei protocols allow the profiling of large cells (<40 µm) that do not fit through microfluidics sequencing methods (Denisenko et al., 2020) and can be combined with gene regulatory studies, for example the assay for transposase-accessible chromatin sequencing (ATAC-seq). Despite all these advantages, an important concern for performing snRNA-seq is that nuclear transcripts represent only a percentage of the total cell’s mRNA, as demonstrated through a study of large and small pyramidal mouse neurons (Bakken et al., 2018). Notwithstanding, numerous studies (mostly in mice) have demonstrated that snRNA-seq is comparable to scRNA-seq; for example, although more transcripts were detected in individual whole cells than nuclei isolated from the primary visual cortex of mice, the independent analysis of the scRNA-seq and snRNA-seq datasets demonstrated that the same neuronal subtypes can be similarly discriminated with both sequencing methods (Bakken et al., 2018). Similar results were observed in another study where both sequencing methods were compared using samples isolated from mouse whole brain cortex (Ding et al., 2020), suggesting that snRNA-seq is useful and reliable for cellular diversity characterization of brain tissues. Hence, this method has been used extensively to profile and classify mouse brain cells (Yao and Liu, 2021). Regarding transcriptome coverage, researchers have demonstrated that snRNA-seq of mouse neural stem cells and hippocampal neurons detects 66% and 82% of the protein coding genes respectively, while the amount of gene expression variation was similar when measured between single nuclei and single cells (Grindberg et al., 2013). Furthermore, in human organs outside the nervous system such as kidney and pancreas, single-cell and single-nucleus methods yielded equivalent gene detection sensitivity with a significant percentage of transcripts detected in scRNA-seq and not detected in snRNA-seq corresponding to mitochondrial and stress-induced genes (Wu et al., 2019; Basile et al., 2021).

Once a single-cell/nucleus suspension is obtained, a method to physically separate them is used. Two common separation methods are plate-based cell sorting and droplet-based microfluidics (Figure 1B). In plate-based methods, cells or nuclei are isolated by fluorescence activated cell sorting (FACS) or nuclei sorting (FANS) into wells (Tasic et al., 2016) where lysis and library preparation are performed. With this method, samples can be stained to enable the exclusion of dead cells or enrichment of cells labeled with specific antibodies against cell surface or intracellular markers (Baran-Gale, Chandra and Kirschner, 2018). Droplet-based methods separate cells into nanoliter-size lipid droplets, where cells are lysed and library preparation takes place (Macosko et al., 2015). Library construction consists of capturing transcripts, labeling them with cell or nuclei-specific barcodes, reverse-transcribing them into cDNA, and amplifying them. Additionally, protocols may also label captured transcripts with unique molecular identifiers (UMIs) before amplification, this approach results in more accurate quantification of counts (Zheng et al., 2017). UMIs are random oligonucleotide barcodes of a fixed length and they are used to tag original transcripts and distinguish them from PCR duplicates (Islam et al., 2014). Droplet-based sequencing methods use lower reaction volumes and yield higher throughputs compared to plate-based methods (Forsberg et al., 2018). However, due to methodological differences, these technologies differ in the way they quantify transcripts. Droplet-based methods incorporate UMIs to either 5′ or 3′ ends, thus, they do not distinguish between gene isoforms. Contrastingly, plate-based methods capture the full-length transcripts including exons and splice junctions (Tasic et al., 2016; Gupta et al., 2018). Another decision to consider is the addition of spike-in RNA. Spike-ins are non-biological RNA molecules of known sequence which are added to each cell’s lysate in the same known concentration (Jiang et al., 2011). Spike-ins undergo all library preparation processes and thus they are very useful for normalization under the assumption that they are present in every cell in the same amount (Stegle, Teichmann and Marioni, 2015). However, the incorporation of spike-ins is not easy. For example, the concentration of the added spike-ins must be precisely calibrated to obtain optimal results, since small variations may lead to biased estimation. Spike-ins should be considered to contribute to 1%–5% of the total number of mRNA molecules in the sample (Robinson and Oshlack 2010; Grün and van Oudenaarden 2015). Furthermore, spike-ins are prone to degradation and may be captured less efficiently than endogenous transcripts in scRNA-seq/snRNA-seq experiments. Additionally, the inclusion of spike-ins is often complicated when using droplet-based separation methods (Haque et al., 2017; Svensson et al., 2017). Therefore, careful consideration should be taken when selecting the separation method.

Sequencing datasets, whether derived from single cells or nuclei are represented by matrixes in which rows correspond to features (e.g. thousands of genes or transcripts) and columns to barcodes (cells or nuclei). Data analysis may be challenging and requires the implementation of sophisticated computational methods some of which have different statistical assumptions (Kharchenko, 2021). Moreover, computational standards for single-cell/nucleus sequencing data analysis are still lacking, whereby researchers must be very careful when selecting the appropriate computational analysis methods. Excellent reviews focused on detailing the best practices for computational data analysis and interpretation have been published (Luecken and Theis, 2019; Andrews et al., 2021; Slovin et al., 2021). In this section, we will describe some of the more general steps required for analyzing scRNA-seq or snRNA-seq datasets (referred to as single-cell datasets because the same type of computational analysis is applied for both sequencing methods) and their downstream analysis.

Alzheimer’s disease (AD) is the most common cause of dementia worldwide with a prevalence expected to reach 113 million by 2050 (Wu et al., 2017). AD is clinically characterized by a progressive decline in memory, language, problem-solving and cognitive skills (Dubois et al., 2010). The core pathological hallmarks of AD are the extracellular deposition of β-amyloid (Aβ) plaques and the accumulation of neuronal fibrillary tangles (NFTs). NFTs are aggregates of Tau protein and Apolipoprotein E. The accumulation of Aβ plaques and NFTs is cytotoxic and causes synapse and neuron loss. Consequently, another pathological hallmark of AD is a widespread neuronal loss specifically in the cortex and hippocampus (McKhann et al., 1984). Increasing evidence suggests that neuroinflammation is also a major contributor to the pathogenesis of AD (Heneka et al., 2015). Pro-inflammatory cytokines, markers of reactive astrocytes and activated microglia, have been found in AD brains of both postmortem humans and transgenic mouse models (Bamberger et al., 2003; Olabarria et al., 2010; Medeiros and LaFerla, 2013). However, the use of anti-inflammatory drugs to treat AD has been unsuccessful mainly because both microglial activation and reactive astrogliosis are complex multi-stage processes that yield diverse phenotypes (Monterey et al., 2021). Thus, it is evident that neuroinflammation is a complex response that involves diverse cell types (neurons, astrocytes, and microglia) with different molecular alterations as well as the crosstalk between them.

Microarray and bulk RNA-seq studies of AD brains have revealed downregulation of neuronal functions and upregulation of immune responses (Heneka et al., 2015; Canter, Penney and Tsai, 2016; De Strooper and Karran, 2016; Nativio et al., 2018). Thus, interactions between the triad (neurons, astrocytes, and microglia) are an important part of the pathophysiology of AD. Other pathways found to be dysregulated are energy metabolism, synaptic transmission, myelin-axon interactions, cytoskeletal dynamics, and protein misfolding (Ginsberg et al., 2000; Colangelo et al., 2002; Blalock et al., 2004; Miller, Oldham and Geschwind, 2008). It is important to note that AD pathology starts in brain regions involved in learning, memory, perception, and emotion (entorhinal cortex, amygdala, and hippocampus) and progresses throughout the cortex (Braak and Braak, 1991, 1996). Interestingly, the hippocampus depicts regional vulnerability with CA1 pyramidal neurons more severely affected than CA2, CA3, and CA4 neighbors (Padurariu et al., 2012). Dissecting the cellular identities and correlating with their vulnerability to degeneration and their contribution to neuroinflammation is fundamental for understanding AD pathogenesis and it can open new avenues for the development of therapeutics. Here, we review recent pioneering single-cell and single-nuclei human transcriptomic studies aimed at studying the cellular heterogeneity involved in Alzheimer’s disease (Table 1).

There are several limitations to performing scRNA-seq/snRNA-seq from frozen postmortem brains, for example, the difficulty of acquiring postmortem brain samples and the low quality due to the degradation of mRNA. The quality of RNA is typically evaluated using the RNA integrity number (RIN), which is the result of numerous factors, including postmortem interval (PMI: time between death and brain processing), patient’s medical condition previous to death, storage conditions, as well as storage time (White et al., 2018). The mRNA degradation is tissue-specific, transcript-specific, and molecule-specific (Nagy et al., 2015; Sobue et al., 2016; Zhu et al., 2017). As expected, transcripts with a lower expression will degrade faster; this is why performing snRNA-seq on postmortem brains is more challenging than bulk RNA-seq. Studies have also demonstrated that miRNAs have a higher PMI-dependent resistance to degradation than other biomolecules (Nagy et al., 2015). There is no clear correlation between the PMI and RIN values obtained from postmortem brain samples. Thus, the general recommendation when requesting tissue from a brain bank is to require similar RIN values, rather than PMI.

Severely affected brain regions in AD are characterized by massive neuronal loss. Therefore, sequencing regions with a high neuronal deficit may bias cell proportions since they rely on assigning cells to clusters that might not have a correct representation. This problem was solved by Mathys et al. in one of the first studies that provided insights into the heterogeneity of AD in cortical regions with single-cell resolution (Mathys et al., 2019). Researchers profiled 80,660 nuclei isolated from prefrontal cortices obtained from 48 human postmortem brains with varying degrees of AD pathology, including controls. Interestingly, the analysis of cell profiles from all combined samples yielded cell types, markers, and cell-type proportions highly consistent with another human cortex snRNA-seq study of neurotypical controls (Lake et al., 2018). Mathys et al. found a total of 8 major transcriptional clusters(each with subclusters) including excitatory neurons, inhibitory neurons, astrocytes, oligodendrocytes, microglia, oligodendrocyte precursor cells (OPC), endothelial cells, and pericytes. When comparing samples with AD-pathology against no-pathology, 1031 DEGs were found in total in different cell types (Mathys et al., 2019). DEGs specific to excitatory and inhibitory neurons were mostly downregulated whereas oligodendrocytes, astrocytes, and microglia depicted more than 50% of upregulated DEGs. Notably, authors found transcriptional dysregulation due to AD pathology in all cell types, with sex-dependent differences. Dysregulation was highest in the early AD stages, and it was mostly cell-type-specific. Conversely, in late AD stages, upregulated DEGs were common to several cell types denoting a global response (Mathys et al., 2019).

One of the first cortical regions that depict neurofibrillary inclusions and neuronal loss at the early stages of AD is the entorhinal cortex. Grubman et al. characterized the heterogeneity underlying this early affected region using postmortem human brains (Grubman et al., 2019). Authors sequenced 13,214 nuclei isolated from entorhinal cortex tissue, dissected from 6 AD individuals and 6 sex and age-matched controls. Their results demonstrated the presence of all 6 known brain cell types (microglia, astrocytes, neurons, OPC, oligodendrocytes, and endothelial cells) of which astrocytes, endothelial cells, and microglia showed the highest gene expression differences between controls and AD. Researchers also demonstrated that specific transcription factors have alterations in opposite directions in different cell subpopulations. For example, in AD, APOE was found downregulated in OPC and upregulated in astrocytes and microglia (Grubman et al., 2019). This is an example of the advantage of using snRNA-seq over bulk RNA-seq; it is possible to detect transcripts with opposite regulation.

Damage to the parietal lobe is common in the early stages of AD, however, its role in the development of AD remains elusive. Del-Aguila et al. transcriptionally profiled single-nuclei isolated from the parietal lobes of an individual carrying a known mutation in PSEN1 gene (p.A79V) and 2 relatives with sporadic AD (Del-Aguila et al., 2019). Samples were obtained from postmortem brains and a total of 30,000 nuclei were sequenced. Authors identified and annotated 14 clusters including 6 subclasses of excitatory neurons, 2 inhibitory neurons, oligodendrocytes, astrocytes, microglia, OPC, and endothelial cells. A reduced proportion of excitatory neurons was observed after comparing the proportion of cells in each cluster between the mutation carrier sample against the sporadic AD samples (Del-Aguila et al., 2019). Furthermore, Del-Aguila et al. performed a pseudo time gene expression reconstruction using TSCAN (Ji and Ji, 2016). This method was implemented using the transcriptomic profile of microglial cells and it allowed authors to capture the sequence of activation and transition to disease-associated microglia (DAM) cells. The expression profiles of DAM cells were obtained from a previous study (Keren-Shaul et al., 2017). Interestingly, the authors found that 79 genes of the DAM markers were significantly associated with a temporal trajectory. This is the first study that analyzed DAM markers in single-nuclei microglia profiles obtained from AD brains carrying a known mutation.

A recent pioneering study adopted an interesting strategy to delineate the progression of AD in specific cell types (Leng et al., 2021). Leng et al. isolated 42,528 and 63,608 single nuclei from the entorhinal cortex and superior frontal gyrus respectively; samples were obtained from 10 human postmortem brains with varying degrees of AD-tau neuropathological progression (inferred from the Braak stage). The entorhinal cortex and superior frontal gyrus regions were selected because they are affected in the early and late stages of AD correspondingly (Braak and Braak, 1991). Leng et al. found 9 and 11 subpopulations of excitatory neurons in the entorhinal cortex and the superior frontal gyrus respectively depicting region-specific genes. Authors found subpopulations of neurons in both regions which were more vulnerable to degeneration in the early and late stages. Furthermore, marker genes potentially responsible for these selective vulnerabilities were derived and validated. For example, the relative abundance of a subpopulation of excitatory neurons in the entorhinal cortex showed a striking decrease in Braak stage 2 samples compared to Braak stage 0, suggesting their susceptibility to neurodegeneration in early stages (Leng et al., 2021). By correlating the degree of degeneration with the region, cell subpopulations, transcriptional profiles, and relative abundances, researchers were able to determine subpopulation selective vulnerabilities.

Given that mounting evidence suggests a microglial role in aging and AD pathology, Olah et al. profiled 13,368 single live cells isolated from the dorsolateral prefrontal cortices of autopsy samples from individuals with mild cognitive impairment or AD pathology (Olah et al., 2020). Additionally, 2,874 cells used as controls were isolated from temporal neocortices of individuals undergoing intractable epilepsy surgeries. The authors used a previously published protocol involving FACS to purify microglial cells using antibodies for CD11b and CD45 (Olah et al., 2018). Single cells were isolated using droplet technology (Olah et al., 2020). Interestingly, researchers found 9 distinct microglial clusters and validated 4 of them histologically using marker genes. Since samples were obtained from individuals without dementia, the authors used a gene set enrichment approach to link microglial subclusters to diseases. One microglial cluster was found to be altered in AD and it was validated in the single nucleus RNA-seq study by Mathys et al. (Mathys et al., 2019).

Parkinson’s disease (PD) is one of the most common slowly progressive neurodegenerative movement disorders but is very challenging at advanced stages. A histological hallmark of PD is the accumulation of fibrillar aggregates called Lewy bodies, enriched in α - Synuclein misfolded protein (Wakabayashi et al., 2007). Another important characteristic of PD is the degeneration of dopaminergic neurons (DaNs) in the substantia nigra pars compacta (SNpc). Loss of these neurons leads to PD motor symptoms such as rigidity, resting tremor, slowness in movement (bradykinesia), and postural instability (Bloem, Okun and Klein, 2021).

Midbrain DaNs have important roles in the regulation of voluntary movement, reward, and emotion. These brain cells are highly heterogeneous even though they share a common neurotransmitter phenotype and lie in close proximity within the ventral midbrain. The current classification of DaNs is based on topographical features, for example, anatomical location and axonal innervation targets. Traditionally, three distinct types of midbrain DaNs are considered: A8, A9, and A10 located in the retrorubral field (RRF), SNpc, and ventral tegmental (VTA) areas respectively (Bentivoglio and Morelli, 2005). DaNs depict heterogeneous susceptibilities to neurodegeneration, specifically to PD. A9 DaNs project their axons into the dorsal striatum through the nigrostriatal pathway, and they are involved in the control of involuntary movement. Although A9 DaNs are the primarily degenerated cell type in PD (Lees, Hardy and Revesz, 2009), other cell types such as astrocytes and microglia have also been implicated in neurodegeneration and PD pathogenesis [reviewed in (Brück et al., 2016; Booth, Hirst and Wade-Martins, 2017)]. A8 and A10 DaNs depict projections into the ventral striatum and the prefrontal cortex, and they are involved in the regulation of emotion and reward. Degeneration of A8 and A10 DaNs is associated with schizophrenia, drug addiction, and depression (Tzschentke and Schmidt, 2000), highlighting the different functions of dopamine in specific brain regions. An important characteristic of human DaNs is that they have up to one million axon terminals, many of which reach long distances from the soma, with projections to the putamen, and the caudate nuclei, compared to neocortical neurons which have up to tens of thousands of synapses (Bolam and Pissadaki, 2012). Thus, this uniquely massive, unmyelinated axonal branching renders ventral midbrain DaNs under a high energy demand compared to neurons from other regions contributing to their vulnerability (Pissadaki and Bolam, 2013). The molecular mechanisms that underlie the phenotypic and functional differences between ventral midbrain DaNs are largely unknown. Limited access to all these brain regions makes single-cell studies experimentally challenging. Here, we review recent pioneering single-cell and single-nuclei human transcriptomic studies aimed at elucidating the cellular heterogeneity involved in Parkinson’s disease (Table 2).

Researchers have contributed to profiling the cellular heterogeneity with single-cell resolution of the SN using postmortem brains of both healthy and PD individuals. One of the earliest single-nuclei transcriptomic studies of the SN was published by Agarwal et al. (Agarwal et al., 2020) who sequenced 10,706 and 5,943 nuclei from the middle frontal gyrus and SN, respectively. Researchers obtained 12 region-matched samples from 5 human postmortem brains without neurological disease. A transcriptomic cellular atlas was compiled from the identification of cell-type-specific gene expression patterns in 10 distinct cell subpopulations found in SN: 2 types of astrocytes, 3 subtypes of oligodendrocytes, endothelial cells, microglia cells, oligodendrocyte precursor cells, DaNs, and GABAergic neurons. Significant associations were found between PD genetic risks (obtained from GWAS) and specific SN subpopulation gene profiles (DaNs and oligodendrocytes) (Agarwal et al., 2020).

In another study, researchers developed a computational method called LIGER (linked inference of genomic experimental relationships) to enable the combination and analysis of single-cell datasets from different individuals, species, regions, conditions, and molecular origin (genomic, epigenomic, and spatial) (Welch et al., 2019). To test their method, Welch et al. generated snRNA-seq from 44,274 single-nuclei isolated from the SN of 7 frozen postmortem brains (healthy controls). A total of 24 cell clusters were identified: 3 subtypes of astrocytes, fibroblasts, mural cells, endothelial cells, 3 microglia types, 7 neuron classes including DaNs, 5 oligodendrocytes, and 2 oligodendrocyte precursor cell subtypes (Welch et al., 2019). Next, the authors compared their SN dataset with a published single-cell dataset obtained from the SN of healthy mice (Saunders et al., 2018) and found a high correlation in the cell-type-specific expression patterns between these species. Gene ontology (GO) analysis of highly correlated human-mouse gene pairs yielded significantly enriched gene sets related to brain cell identity and molecular functions (Welch et al., 2019). Conversely, gene pairs with the lowest correlation were enriched in DNA repair and chromatin remodeling functions, suggesting species differences in epigenetic regulatory functions (Welch et al., 2019). Although this comparison between species was performed on healthy individuals, the approach proposed by this study can be implemented in future studies to compare samples obtained from rodent models of PD with samples obtained from PD human patients (Duty and Jenner, 2011). Moreover, regarding the comparison between species, Geirsdottir et al. demonstrated through single-cell analysis and by using ortholog conjectures that human microglial cells depict significant heterogeneity when compared among other vertebrate species, and in addition, microglia-specific gene expression profiles depicted significant susceptibility for PD and AD (derived from human GWAS) in primates and humans (Geirsdottir et al., 2019).

Additionally, in a recent study, researchers profiled 41,435 single nuclei from postmortem midbrains of 6 individuals diagnosed with idiopathic PD (IPD) and 5 age and sex-matched controls (Smajić et al., 2021). In this study Smajic et al. demonstrated that the dysregulation in IPD is not exclusive to DaNs of the SN, but also different cell types show alterations in other brain regions. Authors found a neuronal cell cluster present only in IPD midbrains, concomitant to reduced numbers of oligodendrocytes in IPD samples. Moreover, an increased microglial subpopulation of the SN in IPD depicted an amoeboid shape, suggesting an activated state (Smajić et al., 2021).

Another comprehensive study recently available was performed by Kamath et al., 2021, (Kamath et al., 2022). This is the first study that profiled hundreds of thousands of nuclei (184,673 and 202,810) obtained from postmortem midbrains of both controls and individuals diagnosed with PD or Lewy body dementia (LBD). Researchers purified nuclei using fluorescence-activated nuclei sorting (FANS) and a nuclear receptor (NURR1 or NR4A2) previously found to be essential for the survival of DaNs in mice (Zetterström et al., 1997; Saunders et al., 2018). The deep sequencing strategy and the data analysis method (LIGER) used allowed researchers to identify 10 clusters of DaNs in both controls and PD/LBD individuals. Interestingly, the authors used a method to accurately determine cell proportions thus, eliminating technical confounders, for example, batch effects and individual variations (MASC: mixed effect association of single cells), and found that one of the DaNs clusters was significantly reduced in PD samples, while another one was increased. These observations were validated through single-molecule fluorescence in situ hybridization (smFISH). Overall, these results confirm the existence of diverse subpopulations of DaNs with different degeneration susceptibilities.

The development of iPSCs and their application for disease modeling has resulted in powerful methodologies to study human complex pathologies. Reprogramming somatic cells from individuals carrying known PD-related mutations to iPSCs has been used to differentiate DaNs in vitro, generating a valuable source of cells that would otherwise be accessible only from postmortem brains, where the progression of PD is already at its endpoint. Thus, modeling PD in vitro through the differentiation of iPSCs to form DaNs, allows the discovery of biomarkers at the onset of disease, and facilitates drug testing (Laperle et al., 2020). However, one limitation of such studies is the high degree of heterogeneity and cellular variability. Variability has been reported in several aspects including iPSC differentiation potential, DNA methylation, transcriptional profiles, and even cell morphologies due to differences between donors, experiments, and genetic stability (Kilpinen et al., 2017; Volpato and Webber, 2020). To overcome these limitations, scRNA-seq and snRNA-seq are currently being used first, to compare the similarity of cells differentiated from iPSCs with those found in vivo, and then to elucidate the cellular heterogeneity at the onset of PD by using dopaminergic neurons differentiated from iPSCs. In this context, La Manno et al. demonstrated through scRNA-seq that dopaminergic neurons generated from human iPSC recapitulated key stages of in vivo midbrain development, retained the expression of markers genes, and conserved the cellular heterogeneity observed in vivo (La Manno et al., 2016).

In a recent study by Fernandes et al., researchers generated a common PD mutation (A53T in SNCA gene) in a human iPSC line (Human induced pluripotent stem cell initiative, Sanger Institute) and induced both WT and A53T-SNCA iPSC into DaNs using a widely accepted induction protocol (Fernandes et al., 2020). After 6 weeks, more than 15,000 cells were profiled from scRNA-seq, and using the expression of known marker genes, 6 clusters were found: 2 neuronal progenitors, and 4 dopaminergic neuron subpopulations. Also, authors found overlapping clusters between their clusters and those found in a study of substantia nigra from 7 human adult postmortem brains (Welch et al., 2019), particularly in clusters characterized by the positive expression of Tyrosine Hydroxylase, thus suggesting that in vitro DaNs are representative of corresponding in vivo DaNs. Finally, induced cells were tested with cytotoxic drugs and genetic stressors and the proportion of cells in clusters changed, suggesting that clusters presented varying degrees of sensitivity (Fernandes et al., 2020).

To overcome the difficulties inherent to cellular heterogeneity of iPSC neuronal induction, Lang et al. sorted iPSC-derived DaNs using FACS with the Tyrosine Hydroxylase intracellular marker (Lang et al., 2019). The authors derived iPSCs and performed both bulk and single-cell RNA-seq from two individuals carrying a known PD mutation (GBA-N370S) and three controls. Although the number of profiled cells after sorting was reduced (146 cells), authors were able to identify DEGs. Notably, cells segregated between control and PD along the second component of the PCA plot. By combining DEGs found with bulk and scRNA-seq and clustering single cells with the Single-cell consensus clustering (SC3) method (Kiselev et al., 2017) authors obtained a core set of 60 DEGs, with the majority downregulated in PD. Using this small set of core genes, the authors implemented the Ouija method to infer single-cell pseudo times (Campbell and Yau, 2019). As a result, authors suggested a “continuous disease axis” of gene expression variation with 60 core genes mostly downregulated. The authors used ingenuity pathway analysis (IPA) (Krämer et al., 2014) to build regulatory gene networks and found that the Histone deacetylase HDAC4 was the common early repressor of the core set of genes. Pharmacological modulation of HDAC4 rescued PD-related phenotypes, including ER stress (Lang et al., 2019). These results show that even with a low number of cells, it is possible to find molecular drivers of a specific PD phenotype, through purification before sequencing, and a comprehensive data analysis using the appropriate computational methods.

Cerebral organoids have emerged as a useful 3D model for neural differentiation studies since they recapitulate certain aspects of brain development and disease-associated phenotypes (Lancaster et al., 2013). For example, by comparing neural precursors differentiated in 2D vs. 3D by scRNA-seq, it was recently established that midbrain organoids presented lower levels of cellular senescence and mitochondrial stress, which correlated with resistance to toxic challenges, robust synaptic contacts, and functionality of DaNs (Kim et al., 2021). Furthermore, another study found by single-cell analysis demonstrated that midbrain organoids maintain the same neuronal heterogeneity observed in vivo, indicating that organoids are a useful system for the in vitro study of neurodegenerative diseases (Smits et al., 2020). Thus, pathological features have been analyzed in brain organoids generated from isogenic iPSCs that carry a PD-related mutation in LRRK2. In such context, scRNA-seq experiments showed clear differences between the normal and mutated iPSCs. The LRRK2 p.Gly2019Ser mutation disrupted normal development, resulting in incomplete differentiation and reduced viability (Zagare et al., 2022). These studies show that midbrain organoids and scRNA-seq constitute an excellent system to study different cellular and molecular aspects related to PD.

In the last decade, impressive progress has been achieved through the use of scRNA-seq/snRNA-seq to elucidate the transcriptional and regional heterogeneity of brain cells. These developments have transformed our understanding of brain cell diversity and their interplay in neurodegenerative disease conditions. Even though methods for scRNA-seq/snRNA-seq have been well developed, several limitations and challenges remain. One example is the source of brain cells. Fresh brain samples mainly from resection surgeries are scarce and the best alternatives are either postmortem brains or differentiated iPSCs. The integrity of the nuclear transcripts obtained from postmortem brains must be carefully determined and brain samples should have similar RIN values. Also, it is important to have access to brain donors’ clinical information including the degree of neurodegeneration to adequately form groups or model covariables. A well-designed experiment including age, sex, and degree of degeneration-matched controls is very important to reduce the number of variables affecting gene expression.

Additionally, since AD and PD are characterized by progressive neurodegeneration and loss of neurons in different regions, identifying the cell subtypes whose proportions are altered in AD or PD is challenging. Researchers rely on algorithms that automatically preprocess and normalize the datasets assigning cells to clusters. However, different algorithms or parameter choices can lead to divergent results. Careful consideration must be employed when selecting which methods to use for data analysis and experimental validation may be needed. Furthermore, the preparation of single cells or nuclei in suspension is also a major challenge when sequencing transcripts obtained from brain cells or nuclei. Particularly for single-cell suspensions, it is difficult to isolate intact brain cells because they are embedded in a complex and interconnected network. Also, neurons are very sensitive to cell dissociation methods, therefore scRNA-seq datasets may show an underrepresentation of neuronal types with respect to glial cells (Darmanis et al., 2015). Therefore, since nuclei isolation protocols are faster and they represent the best alternative when the tissue is degraded, frozen or formalin-fixed (Lacar et al., 2016), snRNA-seq is an excellent alternative for profiling of brain cells. In addition, studies have demonstrated that snRNA-seq results are comparable to scRNA-seq results (Bakken et al., 2018).

Another important caveat of scRNA-seq and snRNA-seq is that by isolating cells or nuclei, their spatial information and connectivity are lost. Transcriptional profiles can convey information about a cell’s identity and the interplay with other cells, however, the information about neighboring cells or their proximity to pathologies (e.g. tau fibrillary tangles, Lewy bodies) is unknown. To address this deficiency, an emerging high-throughput alternative is spatial transcriptomics (Ståhl et al., 2016). Spatial transcriptomic methods are now being used to resolve the location of cells, for example, smFISH and Slide-Seq (Raj et al., 2008; Rodriques et al., 2019). However, it is important to consider that through spatial transcriptomics it is difficult to obtain conclusions with single-cell resolution because these methods consist of profiling spots distributed throughout a histological sample and each spot covers various cells (Noel et al., 2022). Furthermore, the analysis of spatial transcriptomics data can be challenging since detailed computational methods for their study do not exist or are still in development (Atta and Fan, 2021). Thus, in the near future, scRNA-seq/snRNA-seq technologies should be combined with other multi-omics and with spatial transcriptomics to provide a holistic understanding.

Overall, AD and PD brain scRNA-seq and snRNA-seq are providing new insights, for example, identifying cell subpopulations that are more vulnerable to degeneration and cellular transcriptional profiles specific to the affected regions. Particularly, the identification of cell subpopulations and the overall characterization of cell heterogeneity in brain regions affected by AD or PD is valuable for understanding why some neuronal or glial cell types are more susceptible to degeneration than others, as well to better understand how gene expression is regulated at the single-cell level in a pathological state of the brain. Besides, the comparison of the information obtained from single-cell datasets with data obtained, for example, from GWAS, can provide insights for the identification of specific cell subtypes or genes that may provide the risk of neurodegenerative diseases development. The construction of cell trajectories can show the progression of cellular degeneration as the disease progresses, and infer cell types that are lost while others are enriched. Thus, future advancements in this area will facilitate the discovery of potential and novel therapeutic targets.