Научная статья на тему 'RNA Sequencing and Spatial Transcriptomics in Traumatic Spinal Cord Injury (Review)'

RNA Sequencing and Spatial Transcriptomics in Traumatic Spinal Cord Injury (Review) Текст научной статьи по специальности «Биотехнологии в медицине»

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spinal cord injury / RNA sequencing / spatial transcriptomics

Аннотация научной статьи по биотехнологиям в медицине, автор научной работы — Yu.A. Chelyshev, I.L. Ermolin

In order to understand the fundamental mechanisms of the spinal cord functioning, it is necessary to reveal a complete set of cell types and their populations, which can be identified by the unique combination of their features. The technologies of single-cell and single-nucleus RNA sequencing serve as effective tools for determining the role of various types of cells in normal and pathological reactions in the spinal cord. Spatial transcriptomics combines these technologies with the methods of obtaining and saving spatial information about cells in the tissue, which allows one to localize more precisely the injured area, characterize in detail the tissue compartments in the specific anatomical region, and analyze the pathological picture at the cellular and molecular level. Atlases of development of RNA-sequencing technologies and spatial transcriptomics created on the basis of the data from single-cell and single-nucleus RNA sequencing open great opportunities for new perspective concepts concerning the mechanisms of rearranging neural connections and restoration of sensorimotor functions in traumatic spine injury. The transcriptomes obtained were a powerful resource for detecting new functions of the nervous tissue cells. To establish therapeutic targets, the detected molecular diversity in neurons of various types enables tracing and comparing their susceptibility and regenerative potential. Determination of causes of selective cell susceptibility in spinal cord injury needs comprehensive information on the specificity of human cell populations in comparison with the known data obtained on the experimental models. In the present review, we have summarized advances in identification and study of cell characteristics in a traumatized spinal cord based on transcription profiling at a single-cell or single-nucleus level.

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Текст научной работы на тему «RNA Sequencing and Spatial Transcriptomics in Traumatic Spinal Cord Injury (Review)»

RNA Sequencing and Spatial in Traumatic Spinal Cord Injury (Re)

DOI: 10.17691/stm2023.15.6.08 Received August 30, 2023

Yu.A. Chelyshev, MD, DSc, Professor, Department of Histology1; I.L. Ermolin, DSc, Professor, Department of Histology with Cytology and Embryolog

1Kazan Federal University, 18 Kremlyovskaya St., Kazan, the Republic of Tatarstan, 42C 2Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Square, Nizhny Novg 603005, Russia

In order to understand the fundamental mechanisms of the spinal cord functioning, it is necessary to reveal a complete se and their populations, which can be identified by the unique combination of their features. The technologies of single-cell and sir RNA sequencing serve as effective tools for determining the role of various types of cells in normal and pathological reactions in cord. Spatial transcriptomics combines these technologies with the methods of obtaining and saving spatial information about cells ir tissue, which allows one to localize more precisely the injured area, characterize in detail the tissue compartments in the specific anati region, and analyze the pathological picture at the cellular and molecular level.

Atlases of development of RNA-sequencing technologies and spatial transcriptomics created on the basis of the data from single-cell and single-nucleus RNA sequencing open great opportunities for new perspective concepts concerning the mechanisms of rearranging neural connections and restoration of sensorimotor functions in traumatic spine injury. The transcriptomes obtained were a powerful resource for detecting new functions of the nervous tissue cells. To establish therapeutic targets, the detected molecular diversity in neurons of various types enables tracing and comparing their susceptibility and regenerative potential. Determination of causes of selective cell susceptibility in spinal cord injury needs comprehensive information on the specificity of human cell populations in comparison with the known data obtained on the experimental models.

In the present review, we have summarized advances in identification and study of cell characteristics in a traumatized spinal cord based on transcription profiling at a single-cell or single-nucleus level.

Key words: spinal cord injury; RNA sequencing; spatial transcriptomics.

How to cite: Chelyshev Yu.A., Ermolin I.L. RNA sequencing and spatial transcriptomics in traumatic spinal cord injury (review). Sovremennye tehnologii v medicine 2023; 15(6): 75, https://doi.org/10.17691/stm2023.15.6.08

This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Sensory information processing and locomotor reactions in the spinal cord are controlled by neural networks incorporating cells of many types [1-3]. To establish cellular correlates of behavioral reactions, it is important to have knowledge of specific types of cells forming the neural network. The solution of this task became possible owing to the development and application of RNA sequencing (RNA-seq) technologies of a single cell (scRNA-seq) or a single nucleus (snRNA-seq), an effective tool for understanding the role of various types of cells in normal and pathological reactions in the nervous system. These technologies make it possible to present the distribution of the specific type of cells by visualizing gene expression in the context of a tissue structure (spatial transcriptomics).

The investigation of Cahoy et al. [4] served as

a conceptual and technological breakthrough in neurosciences as the first classification of CNS cell populations based on transcriptome profiles created using the results of researches applying microchip technologies. The transcriptomes obtained were a powerful tool for the understanding of new functions of the nervous tissue cells. Later, this classification was specified and complemented using scRNA-seq/ snRNA-seq technologies and spatial transcriptomics.

An understanding of the fundamental mechanisms of spinal cord functioning requires the detection of a complete set of cell types and their populations, which can be identified by the unique characteristic combination. A specific type of the spinal cells is characterized by many parameters such as localization, structure, cytogenesis, electrophysiological properties, involvement in network formation, patterns of gene expression, and involvement in behavioral reactions [5].

Corresponding author: Igor L. Ermolin, e-mail: ermolinigor@mail.ru

It is not obligatory to have a complete set of characteristic features for identification of the specific cell type, but their combination underlies cell identity.

During sample preparation for scRNA-seq, enzymatic cleavage results in the destruction of synaptic structures and neuron death. snRNA-seq, as compared to scRNA-seq, can minimize the generation of pseudocellular structures induced by enzymatic hydrolysis and mechanical damage. Besides, snRNA-seq is supposed to collect information on introns and intergenic regions and characterize rarer types of cells. Although snRNA-seq enables one to obtain more complete information on the cell types, the scRNA-seq technology appeared to be more suitable for the description of some types of cells, for example, immune cells [6, 7].

The methodology of RNA sequencing includes the following stages: 1) dissociation of tissue and obtaining separate cells; 2) amplification of nucleic acids; 3) high-throughput sequencing; 4) data analysis. The detailed characteristic and traps and pitfalls of each stage are considered in the works [8-13].

Spatial transcriptomics combines scRNA-seq with the methods of obtaining and saving spatial information in the tissue. This approach allows one to precisely localize the injured region and to consider its pathogenesis at the cell level [14]. If compared to the standard scRNA-seq technology, the resolution and effectiveness of gene detection in the process of spatial transcriptomics are insufficient, but bioinformatics algorithms give the possibility to achieve the desired approximation.

Methods of spatial transcriptomics are used to create the maps (atlases) of gene expression in any tissue compartments. Such harmonized atlases of cell types and their distribution have already been created for a mouse spinal cord [15-17]. In these atlases, a large panel of markers is engaged for exploring the cells of different types both in vivo and in vitro, the supposed embryonic lines for each type of cells are presented, and computational resources for classification of spinal cord cells based on transcriptomics are also mobilized, allowing the researchers to readily interact and analyze the data on specific cells of the spinal cord [17]. These atlases can serve as a universal nomenclature both of cell types and a set of molecular markers, which together characterize in great detail tissue compartments in a specific anatomical region.

The scRNA-seq/snRNA-seq technologies play an important role for establishing a standard set of cell types in the spinal cord and understanding molecular mechanisms of pathological shifts in this organ. Such technologies enable one to perform screening of the differentially expressed genes at various times after spinal cord trauma (SCT). Thus, in contusion SCT at Th8 level in the rat, the number of genes with significantly changed activity was 944, 1362, and 1421 on days 1, 4, and 7, respectively, after the traumatic event [18]. Using scRNA-seq, hundreds of molecularly

diverse types of cells have been revealed in the mouse and human nervous system, whose function is determined by a differential activity of genes and topography of the specific cell types [16, 19, 20]. Application of the sequencing technology together with spatial transcriptomics makes it possible to understand molecular fundamentals of ontogenetic origins of cell variety in CNS [21-23].

In the given review, we will not delve into the technological aspect of the problem, but consider the main achievements of scRNA-seq/snRNA-seq technologies and spatial transcriptomics in SCT.

Literature search was conducted in PubMed database using the following key words: "spinal cord trauma", "RNA sequencing", "spatial transcriptomics".

Spinal neurons

Spinal neurons are characterized by an intensively marked heterogeneity. A clear understanding has been formed about cytogenetic, structural, cytochemical, and functional characteristics of motor neurons, interneurons, propriospinal, cholinergic, exciting, and inhibitory neurons [24-35]. However, as the results of the recent works with scRNA-seq/snRNA-seq have shown, all these populations appeared to be still more heterogenic in terms of differential gene expression [15, 17, 36-38]. Information on heterogeneity of spinal neuron populations is of great applicational importance for identification of neurons more or less susceptible to damage (disease resistance) in order to find the targets in traumatic injury and neurodegenerative diseases, for example, in lateral amyotrophic sclerosis [14, 39-41].

The results of the spinal cord study engaging the RNA-seq technology enabled the researches to establish the decisive role of neural network topography in the organization of the spinal cord of mammals. Thus, using scRNA-seq, the main differences between dorsal and ventral neurons were defined on the criteria of cluster formation and differential gene expression controlling neuroplasticity [17]. This observation is critically important for understanding the differences in the regenerative potential of the neurons of specific populations in pathologic conditions. The dorsal clusters differ in distinctly divided concrete neuron types, which are easily grouped into families. These neurons are located at a large distance from each other and may be reliably discernable. On the contrary, ventral clusters of neurons are closer to each other, with close or overlapping distribution in the tissue and overlapping patterns of gene expression. These differences in the structural and molecular organization of the dorsal and ventral parts of the spinal grey matter may underlie the specificity of neuron network functioning in these regions. Higher plasticity of connections in the dorsal area may be due to reactions such as central sensitization, progressive enhancement of nociceptive neuron excitability, long-term potentiation, depression,

which accompany chronic neuropathic pain. In the ventral part, connections are structurally more stable, which is evidenced, in particular, by a high gene expression coding the synthesis of perineuronal net components stabilizing synaptic connections and limiting neuron plasticity [17].

The scRNA-seq data are crucially important in cell biology since they allow one to reconceive the boundaries of phenotype and classical definition of the cell belonging to the specific type as the cells with identical set of genes allowed for expression regardless whether they are transcribed or not.

In the study by Blum et al. [37], 43,890 transcriptomes have been profiled in the material of the mature mouse spinal cord, enriched with nuclei of efferent neurons, and a detailed characteristic of gene expression was given at the level of a separate cell. Efferent neurons of the spinal cord make up only 0.4% of all cells in the organ. Therefore, to profile transcriptomes in the snRNA-seq technology, fluorescence-activated sorting and enrichment of nuclei were performed. Cholinergic neurons were presented by 20 clusters. Also, there were identified 16 clusters of sympathetic neurons, which differed in localization and expression of the genes of neuromodulator signaling including several clusters localized in the sacral part of the spinal cord. Here, autonomic neurons of various subtypes express different combinations of neuromodulating peptides such as somatostatin, neurotensin, and proenkephalin. This study allowed for identification of new genetic markers specific for autonomic and somatic motor neurons, for a- and Y-motor neurons, and also establishment of heterogeneity of Y-motor neurons, whose various types express different transcriptional programs [37].

Differences in gene modules have been characterized in detail in electrophysiologically and metabolically various populations of fast and slow a-motor neurons. These types of motor neurons differ in the set of potassium channel subunits, which control resting potentials and excitation speed [24]. Marked transcriptional heterogeneity of somatic motor neurons correlates with electrophysiological characteristics and localization of the motor pools [37].

Heterogeneity of cell populations formed after traumatic spinal cord injury in the lumbar region of mice was comprehensively defined by means of snRNA-seq [15]. In this study, 17,354 nuclei have been sequenced, seven main clusters detected and presented by the following cell types identified by expression of the markers: 52% of neurons, 16% of oligodendrocytes, 14% of a mixed population of meningeal and Schwann cells, 9% of astrocytes, 5% of vascular cells, 1% of oligodendrocyte precursors, and 1% of microglia. Forty-three populations of neurons, which were unevenly distributed across the clusters in different regions of grey matter, have been identified and molecularly described. Thus, 55% of neurons were concentrated in 25 dorsal clusters, 34% — in 13 ventral clusters, and 11% of neurons were localized

in 5 clusters — in dorsal horns and in the intermediate zone. The dorsal part of grey matter contained the cell population, which differed mainly in gene expression, whereas the ventral segment demonstrated the overlapping patterns of gene expression [15].

Motor neurons of the lateral motor nucleus are present in the cervical and lumbar parts of the spinal cord and control extremity muscle contraction, whereas motor neurons of the medial motor nucleus are distributed across the entire rostro-caudal axis of the organ and are connected with the axial musculature. In vertebrates, molecular identity of motor neurons of the mentioned nuclei is generally known. Nevertheless, the identity of subtypes in these cell populations innervating separate groups of muscles remained unclear. The answer to this question was received using snRNA-seq [38]. The motor neurons of the medial motor nucleus are subdivided into three subtypes, which are distinguished by the expression pattern of genes Satb2, Nr2f2, and Bcl11b and depend on the localization of the neurons along the mediolateral axis and expression of molecules controlling the directed axon growth.

When establishing therapeutic targets, identified molecular diversity allows for tracing and comparing susceptibility and regenerative potential of different subtypes of neurons in the models of CNS damage and neurodegenerative diseases, for example, lateral amyotrophic sclerosis [40, 42]. The scRNA-seq/ snRNA-seq technologies have become actively used to analyze molecular and cellular mechanisms of pathologic reactions and regeneration in SCT [11, 43-50].

To find the causes of selective cell susceptibility in CNS pathology, it is important to have detailed information on the specificity of human cell population and compare them to the known data obtained on the experimental models. Using snRNA-seq and spatial transcriptomics, 29 clusters of glia and 35 neuron clusters [51], arranged mainly by the anatomical principle, have been identified. The information resource created on these data is of vital importance for a clinical picture. Spinal motor neurons, which degenerate in lateral amyotrophic sclerosis and other neurodegenerative diseases relative to other spinal neurons have been shown to express genes controlling the cell size and cytoskeleton structure, which suggests the availability of specialized molecular repertoire underlying their selective susceptibility.

Interesting results obtained by Sun et al. [52] are worth mentioning to illustrate the clinical significance of the data on RNA sequencing of the spinal cord cells in mice with the models of neurodegenerative diseases. In this study, the most prominent shifts were found not in the neural cells of the mice with the model of spinal muscular atrophy, as it was expected, but in the subpopulation of vascular fibroblasts [52]. The number of cells in this subpopulation decreased essentially, which led to the vascular defects with the following inhibition of energy metabolism and protein synthesis.

A lumbar spine represents a special interest for the analysis of posttraumatic reactions and is considered as an important therapeutic target regardless of the level of spinal cord injury [53, 54]. It is primarily connected with the presence of interneuron networks in this part forming a central generator of patterns and controlling the motor function [55, 56]. In case of SCT in the lumbar spine, rearrangement of nervous connection occurs alongside with the reorganization of the descending motor pathways [57-59]. Involvement of specific interneurons forming these networks, their belonging to the definite classes of precursors, molecular specificity, and capability of modulating a phenotype in response to the damage remain unclear. The scRNA-seq/snRNA-seq technologies were recognized to be the most informative in solving the complex of these issues in SCT.

Application of snRNA-seq technology made it possible to acquire essentially new data on the mechanisms of regeneration of the motor function in severe contusion SCT in a mouse in response to epidural electrostimulation [48]. The SCT in the middle thoracic segment caused complete disintegration of corticospinal tract fibers and a marked reduction of the amount of glutomatergic fibers of the reticulospinal tract more caudally from the injury region. In the lumbar segment of the spinal cord, of 82,093 nuclei subject to the transcriptome analysis, 20,990 belonged to the neurons which were assigned to 36 populations. Electrostimulation resulted in immediate activation of exciting interneurons localized in the intermediate laminae of the lumbar spinal segment, which expressed Vsx2 gene (Visual System Homeobox 2) and the marker of the caudal spine neurons Hoxa10 (Homeobox A10). Kathe et al. [48] managed to establish in the process of elegant experiments that these neurons were not involved in the control of stepping in the intact spinal cord but were triggered in the regeneration of walking after SCT. The authors believe that a positive effect of epidural electrostimulation on the restoration of the motor function in the clinical picture of 9 patients with SCT might be associated with the activation of exactly these interneurons.

The population of neurons (~21%) prevails in the intact lumbar spinal cord of a mouse. On the model of severe contusion SCT in the thoracic region of the mouse spinal cord, no significant changes in the ratio of populations of exciting, inhibiting, and motor neurons (8:8:1) were registered in the lumbar spinal cord segment by means of snRNA-seq [49]. Meanwhile, signs of enhancement of synaptic plasticity and sensitivity to the action of neurotransmitters are noted in this part of the spinal cord, as well as activation of the genes encoding synthesis of their receptors, synaptogenesis, and remodeling of synapses.

In case of SCT in the thoracic spinal cord segment, neurons of the lumbar region remain generally save in contrast to the neurons in the epicenter of the damage [60, 61]. However, already in a week after the SCT,

the lumbar spinal neurons demonstrate shifts in the expression of the genes encoding the cell stress molecules including redox reactions and protein folding, and also the molecules of neurotransmitter-mediated signaling and ion channel functioning. At the same time, some populations of exciting neurons in the dorsal horns and inhibiting neurons in the ventral horns are characterized by alterations in the organization of synapses and gene expression connected with plasticity [49]. After SCT, physiological cascades in the majority of the lumbar neurons are inhibited and the genes connected with neurotransmission and restructuring of synapses are activated. Unlike these reactions, regeneration-associated genes begin to express in the neurons of two concrete populations, namely, in Shox2-expressing V2d and in the neurons of the spinocerebellar tract after the injury. The data obtained with RNA sequencing give reason to believe that neurons of the lumbar networks belonging to different populations may demonstrate specific strategies of restoration [49].

In SCT, transplantation of neural precursors into the spinal cord stimulates regeneration of axons of corticospinal tract and recovery of the motor function [46, 62]. In order to define molecular mechanisms of such an action, the regeneration transcriptome (reversion to the embryonic state) of motor neurons localized in the layer V of the motor cortex, the axons of which form a corticospinal tract, have been analyzed [46]. Only SCT, as well as the trauma in combination with transplantation of neural precursors, cause similar early transcriptome responses in motor neurons. Two weeks after SCT, this transcriptome is inhibited, but when the trauma is combined with cell transplantation, it appears more stable. Huntingtin gene (Htt) is a central gene-concentrator (hub) in the regeneration transcriptome including regeneration-associated genes and programs [63-67]. Htt deletion significantly weakens regeneration, which indicates a key role of this gene in neuron plasticity after trauma [46].

Astroglia

Heterogeneity of the astrocyte population is generally recognized [68-71]. The scRNA-seq technology widens essentially our notion in this field, allows us to receive new data on the astrocyte population in the spinal cord and their behavior in SCT [72]. Glial fibrillary acid protein (GFAP) is considered the most reliable marker for identification of astrocytes. The scRNA-seq technology made it possible to identify the populations of GFAP-expressing cells in intact, sham-operated, and traumatized mice with spinal cord compression in the caudal thoracic region. Populations of astrocytes different in the expression of gene including those controlling proliferative activity, were defined in the acute and chronic stages. Astrocytes expressing the markers specific only for this cell types and astrocytes, which

along with their markers expressed the markers of ependymal cells, were also identified. The amount of cells with a mixed expression significantly increased in the acute phase of SCT, they were localized at a distance from the central channel both in the intact and traumatized mice. These astrocyte-ependymal cells were present both in white and grey matter, but their quantity in white matter prevailed [72]. The increase in such cells in the acute phase has been also unexpectedly found in the sham-operated animals (with laminectomy only). This observation raises the question of an adequacy of using animals of this group as a control in SCT.

The molecular profile, change in the duration of the cell cycle phases, similar to the radial glia morphology of astrocyte-ependymal cells with high expression of nestin, the marker of neural stem cells [73], not only confirm the data on the origin of one of the astrocyte populations from the neural stem cell, localized in the ependymal layer of the spinal cord, but indicate to the presence of similar cells in the intact spinal cord [72]. Despite the capability of differentiation into astrocytes [74] and oligodendrocytes, ependymal stem cells in SCT seem to be more predisposed to the generation of astrocytes, which make up about half of the total number of astrocytes of the glial scar [75].

Oligodendroglia

Mature oligodendrocytes also show transcription heterogeneity, functional consequences of which are not clear. Their heterogeneity may correlate with the influence of microenvironment or interaction with various neuron types. Some populations of oligodendrocytes in the mammalian CNS have been shown to possess spatial arrangement [44]. Oligodendrocytes type 2 prevail in the spinal cord, whereas oligodendrocytes type 5 and 6 increase their contribution to the oligodendrocyte lineage with age in all analyzed regions of CNS. Oligodendrocytes type 2 and 5/6 differ in the presence in the motor and sensory tracts. Progenitors of oligodendrocytes in neurogenesis seem not to be specified for differentiation into the cells of these populations. Reactivity of oligodendrocytes type 2 and 5/6 is different in chronic SCT. Oligodendrocytes type 2 decrease their contribution into the oligodendrocyte population in the damaged region and increase it in the areas of nerve fiber degeneration, especially in the chronic phase of SCT [44]. Increased presence of oligodendrocytes types 5/6 in the general population in the site of injury indicates that factors, which stimulate resident oligodendrocyte progenitors to preferential differentiation into oligodendrocytes types 5/6, are active in this region. On the whole, scRNA-seq in complex with immunofluorescence analysis show that various populations of oligodendrocytes differ in spatial preferences, differently react to the SCT, and may perform different functions in the course of regeneration.

Two clusters of oligodendrocyte progenitors, A and B, have been identified in acute SCT in the thoracic region using scRNA-seq. Cells with the phenotype A predominate. These cells express classical genes for oligodendrocyte progenitors encoding synthesis of the molecules such as chondroitin sulfate proteoglycan 4 (CSPG4, or NG2-proteoglycan), receptor of platelet-derived growth factor a (PDGFRa), and tenascin R. Oligodendrocyte progenitors B appear on the first day after injury and express actively tenascin C [47]. Oligodendrocyte progenitors A are supposed to be involved in the processes of cytogenesis and myelination, while the cells of population B actively proliferate [47].

Ependymal glia

Neural stem cells in the formed spinal cord are present in the population of the ependymal cells. Their multiplication is not registered in the human spinal cord [76, 77], although in vitro ependymocytes enter mitosis [78-80]. Ependymal glia became the object of experimental studies at the level of a separate cell [45, 81, 82]. In a number of works, data have been obtained on the manifestation of a neural stem cell potential by some population of ependymocytes [82, 83]. Ependymocytes and neural stem cells in the adult organism originate from the common embryonal progenitors [82, 84]. A comprehensive analysis of population heterogeneity and age-related ependymocyte transcriptome in the spinal cord was undertaken in the study [50]. In the general population of ependymocytes, scRNA-seq allowed for identification of immature cells as potential stem cells in the spinal cord. After the trauma, these cells enter again the cell cycle, which is accompanied by a short-term reversion of their maturation.

Resident neural stem cells make a limited contribution to cell replacement. In traumatic injury of the mouse spinal cord, ependymal cells give rise mainly to astrocytes of the glial scar [75, 82] and to a lesser extent to oligodendrocytes [85-87]. A latent potential of the resident neural stem cells for replacing a significant part of the dead oligodendrocytes in the injured mouse spinal cord has been detected. The scRNA-seq technology demonstrates neural stem cells being in the permissive state, which allows realization of the usually latent program of gene expression for oligodendrogenesis after the injury.

In the formed spinal cord, the oligodendrocyte marker, transcription factor Olig2, does not cause a stimulating effect on oligodendrogenesis and myelination, but early progenitors in the ependymocyte population preserve the possibility of the response to the action of this transcription factor, and this is observed in the case of SCT. Ectopic Olig2 expression accompanies intensive oligodendrogenesis, generated from the stem cells, which follows the

natural differentiation of oligodendrocytes, promotes remyelination of axons, and stimulates restoration of nerve fiber conductivity [45]. These data give the opportunity to suppose that recruiting resident stem cells may serve as an alternative to cell transplantation after CNS damage.

Microglia

Microglia is presented by the resident immune cells in CNS, which participate in the immune defense, maintenance of homeostasis, phagocytosis of the dead cell fragments, pruning of excess synapses and axon collaterals, growth stimulation, and remyelination of neuron projections [88-91]. Presently, the scRNA-seq/ snRNA-seq technologies are widely used to study the diversity of microglia [47, 92-95]. RNA sequencing has identified new microglia markers: S100A8, S100A9, HEXB, TMEM119, GPR34, P2RY12, Siglec-H, TREM2, OLFML3 [96].

A separate and difficult task, which was successfully and completely solved owing to the RNA-sequencing technology, consisted in differentiating microglia from macrophages. These cell lineages are characterized by morphological identity and similar phenotypic markers in vivo, especially in pathology including SCT, when both cell types are activated [95, 97-100].

Recent investigations using scRNA-seq have established that microglia differs from the border-associated macrophages by expression of the genes encoding purinergic receptor P2yr, membrane transporter SLC2A5 (solute carrier family 2 member 5), transmembrane protein Tmem119, and p-subunit of p-hexosaminidase (Hexb), whereas border-associated macrophages express the molecule of lymphocyte activation Ms4a7 (member 7 subfamily 4 of the membrane-spanning domain), mannose receptor (Mrc1), and others [95]. These data are important for identifying and studying the role of border-associated macrophages in CNS, which control the delivery of leucocytes from blood and cerebrospinal fluid to the brain parenchyma, and also limit CNS and blood exchange with different cytokines and chemokines [101].

In SCT, microglia coordinate specifically the interaction of different cell types [102]. Pharmacological depletion of microglia aggravates spinal cord injury and worsens function recovery. On the contrary, restoration of intracellular signaling cascades in microgliocytes, identified by the scRNA-seq data, prevents the secondary damage and facilitates regeneration. The analysis of this work [102] shows that optimal recovery after SCT may be achieved through coordination of the key ligand-receptor interaction between microglia, astrocytes, and infiltrating leukocytes.

A spinal cord trauma triggers neuroinflammatory reaction, in which monocytes/macrophages and resident microglia cell predominate. The scRNA-seq technology distinguishes homeostatic and non-homeostatic microglia

in the spinal cord. In SCT, non-homeostatic microglia include three populations, namely, inflammatory, proliferating, and migrating microglia. The inflammatory microglia are characterized by the expression of genes associated with cell death, cytokine production, and expression of the purinergic P2ry12 receptor gene, which weakly expresses in two other populations of non-homeostatic microglia. The proliferating microglia express genes associated with regulation of the cell cycle, for example Cdk1. Microgliocytes of the smallest population of migrating cells express cell mobility-related genes and are characterized by high levels of expression of macrophage scavenger receptor gene (Msr1) and insulin-like growth factor 1 gene (Igf1) [47].

In SCT, spatial transcriptomics reveals phenotypes of monocytes, macrophages of several subtypes, namely those inducing chemotaxis and proinflammatory ones, and border-associated macrophages and dendrite cells in the clusters of myeloid cells [47]. Besides, the mentioned macrophage subtypes do not comply with the well-known classification of polarization phenotypes M1/M2. Inducing chemotaxis and proinflammatory macrophages demonstrated patterns of interaction similar to those in astrocytes and fibroblasts. These intercellular signaling-related data, obtained by means of scRNA-seq, contribute to the understanding of the molecular mechanisms of astrogliosis, fibrosis, and angiogenesis in SCT.

The spinal cord trauma is followed by many reactions of different cell types, which are specific in time, by the terms, and location. In mice traumas, application of scRNA-seq in combination with the traditional analysis of the structure, behavior, and electrical activity made it possible to obtain data on temporal and molecular shifts at the level of a single cell. There were described pathological changes in the cells of 12 main types, of them three types of cells migrated to the spinal cord at different time after trauma [103]. In the intact spinal cord, new subtypes of microglia have been detected with individual dynamic transformations typical for each of them and specific for various stages of the pathological process. Activation of microglia occurs by two waves. The most marked microglia changes are noted on days 3 and 14 after SCT. In the subacute period, when manifestations of neuroinflammation are most dynamic, the microglia reaction is followed by the activation of seven gene-concentrators (hub genes) Itgb1, Ptprc, Cd63, Lgals3, Vav1, Shc1, and Casp4 [104]. By day 38, the main cell types are still deviated significantly from the state in the intact spinal cord [103].

The majority cell types in a traumatized spinal cord return, as a rule, to the initial state, whereas the possibility of constant reprogramming of the molecular profile leading to a prolong change of the immune status in the traumatized spinal cord is considered for microglia. In SCT of mice, microgliocytes with increased expression of regeneration-associated molecules have been detected. Similar cells are typical for newborn mice

but with small differences in gene expression relative to their neonatal analogs [103].

Conclusion

The choice of a therapy mode in the traumatic spinal cord injury requires an understanding of the role of the key genes and the respective intracellular regulatory pathways. However, the selection of single genes for this purpose does not allow one to disclose deep molecular mechanisms of the spinal trauma pathogenesis. The constructive solution of this task began with the application of the scRNA-seq/snRNA-seq technologies.

The results of recent investigations using scRNA-seq/snRNA-seq have shown that all earlier known populations of spinal neurons appeared heterogeneous according to the criterion of differential gene expression. This information has acquired an important clinical significance for identification of the most susceptible neurons in traumatic spinal cord injury. The RNA-sequencing technologies helped define molecular-genetic correlates of functional differences and regenerative potential of neurons localized in the dorsal and ventral laminae, which may be the basis of specific functioning of neural networks in these regions of grey matter. Activation of interneurons was found in the lumbar spinal cord region in response to the remote trauma in the thoracic spine. These interneurons are not involved in the stepping act in the intact spinal cord but are activated after trauma and the following epidural electrostimulation. The RNA-seq data give reason to suppose that the neurons, pertaining to various populations, forming motor networks, may realize different molecular programs of regeneration.

The scRNA-seq/snRNA-seq technologies expand our notion about heterogeneity of spinal astrocytes in traumatic spinal cord injury. They differ in expression of genes controlling proliferative activity. Astrocytes, expressing markers of the ependymal cells, were identified both in white and grey matter. The number of these astrocytes increases significantly in the acute phase of the spinal cord trauma.

Molecular profile, change in phase duration of the cellular cycle, similar to the radial glia morphology of the astrocyto-ependymal cells with a high expression of nestin, the marker of stem cells, not only confirm the data on the origin of one of the astrocyte population from the stem neural cell localized in the ependymal spinal cord layer but point to the presence of the similar cells in the intact spinal cord.

The scRNA-seq data in complex with the immunofluorescence analysis show that spinal oligodendrocytes of various populations are topographically different, differently react to the SCT, and may fulfil diverse functions in the process of regeneration.

The RNA-seq technologies made it possible to establish and analyze in detail heterogeneity of the

population and age dynamics of ependymocyte transcriptome in the spinal cord. Data were obtained on the manifestation of the neural stem cell potential in ependymocytes of some subpopulation and unfolding a normally latent program for oligodendragenesis after spinal cord trauma. These technologies enabled obtaining new data on saving by the early precursors in the ependymocyte population the possibility to respond to the action of transcription factor Olig2, a stimulator of oligodendrogenesis.

The scRNA-seq/sn RNA-seq technologies have revealed new microglia markers, by which activated microglia and macrophages can be reliably discriminated; enabled receiving new data on involvement of microglia in the specific mechanisms of coordinating the interaction between the different types of spinal cells in traumatic injuries of the spinal cord. The scRNA-seq technology provided the opportunity to divide microglia into homeostatic and non-homeostatic and to distinguish in the latter the subpopulations of inflammatory, proliferating, and migrating microglia.

In traumatic spinal cord injury, spatial transcriptomics describes monocyte phenotypes inducing chemotaxis in the clusters of myeloid cells; proinflammatory and border-associated macrophages, and dendrite cells. The data obtained assume the possibility of constant reprograming of the glial molecular profile leading to a prolonged change of the immune status in the traumatized spinal cord.

The development of RNA-sequencing technologies and spatial transcriptomics opens great opportunities for formulation of novel promising conceptions concerning the mechanisms of neural connection reorganization and restoration of sensorimotor functions after the spinal cord trauma.

Authors' contribution: Yu.A. Chelyshev, I.L. Ermolin were equally involved in the analysis of publications and writing the text of the review.

Study funding. The work was financially supported by the grant of the Russian Science Foundation No.23-25-00002.

Conflicts of interest. The authors have no conflicts of interest to declare.

References

1. Côté M.P., Murray L.M., Knikou M. Spinal control of locomotion: individual neurons, their circuits and functions. Front Physiol 2018; 9: 784, https://doi.org/10.3389/fphys. 2018.00784.

2. Ramlrez-Jarquln U.N., Tapia R. Excitatory and inhibitory neuronal circuits in the spinal cord and their role in the control of motor neuron function and degeneration. ACS Chem Neurosci 2018; 9(2): 211-216, https://doi.org/10.1021/ acschemneuro.7b00503.

3. Osseward P.J. II, Amin N.D., Moore J.D., Temple B.A., Barriga B.K., Bachmann L.C., Beltran F. Jr., Gullo M., Clark R.C., Driscoll S.P., Pfaff S.L., Hayashi M. Conserved

reviews

genetic signatures parcellate cardinal spinal neuron classes into local and projection subsets. Science 2021; 372(6540): 385-393, https://doi.org/10.1126/science.abe0690.

4. Cahoy J.D., Emery B., Kaushal A., Foo L.C., Zamanian J.L., Christopherson K.S., Xing Y., Lubischer J.L., Krieg P.A., Krupenko S.A., Thompson W.J., Barres B.A. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci 2008; 28(1): 264-278, https://doi.org/10.1523/jneurosci.4178-07.2008.

5. Dobrott C.I., Sathyamurthy A., Levine A.J. Decoding cell type diversity within the spinal cord. Curr Opin Physiol 2019; 8: 1-6, https://doi.org/10.1016/j.cophys.2018.11.006.

6. Wu H., Kirita Y., Donnelly E.L., Humphreys B.D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol 2019; 30(1): 23-32, https://doi. org/10.1681/asn.2018090912.

7. Ding J., Adiconis X., Simmons S.K., Kowalczyk M.S., Hession C.C., Marjanovic N.D., Hughes T.K., Wadsworth M.H., Burks T., Nguyen L.T., Kwon J.Y.H., Barak B., Ge W., Kedaigle A.J., Carroll S., Li S., Hacohen N., Rozenblatt-Rosen O., Shalek A.K., Villani A.C., Regev A., Levin J.Z. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 2020; 38(6): 737-746, https://doi.org/10.1038/s41587-020-0465-8.

8. Hwang B., Lee J.H., Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 2018; 50(8): 1-14, https://doi.org/10.1038/s12276-018-0071-8.

9. Chen G., Ning B., Shi T. Single-cell RNA-seq technologies and related computational data analysis. Front Genet 2019; 10: 317, https://doi.org/10.3389/ fgene.2019.00317.

10. Slovin S., Carissimo A., Panariello F., Grimaldi A., Bouché V., Gambardella G., Cacchiarelli D. Single-cell RNA sequencing analysis: a step-by-step overview. Methods Mol Biol 2021; 2284: 343-365, https://doi.org/10.1007/978-1-0716-1307-8_19.

11. Cao Y., Zhu S., Yu B., Yao C. Single-cell RNA sequencing for traumatic spinal cord injury. FASEB J 2022; 36(12): e22656, https://doi.org/10.1096/fj.202200943r.

12. Jovic D., Liang X., Zeng H., Lin L., Xu F., Luo Y. Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med 2022; 12(3): e694, https://doi. org/10.1002/ctm2.694.

13. Liu Y., Liang S., Wang B., Zhao J., Zi X., Yan S., Dou T., Jia J., Wang K., Ge C. Advances in single-cell sequencing technology and its application in poultry science. Genes (Basel) 2022; 13(12): 2211, https://doi.org/10.3390/genes13122211.

14. Skinnider M.A., Gautier M., Yue A., Teo Y., Kathe C., Hutson T.H., Laskaratos A., de Coucy A., Regazzi N., Aureli V., James N.D., Schneider B., Sofroniew M.V., Barraud Q., Bloch J., Anderson M.A., Squair J.W., Courtine G. The Tabulae Paralytica: multimodal single-cell and spatial atlases of spinal cord injury. bioRxiv 2023, https://doi. org/10.1101/2023.06.23.544348.

15. Sathyamurthy A., Johnson K.R., Matson K.J.E., Dobrott C.I., Li L., Ryba A.R., Bergman T.B., Kelly M.C., Kelley M.W., Levine A.J. Massively parallel single nucleus transcriptional profiling defines spinal cord neurons and their activity during behavior. Cell Rep 2018; 22(8): 2216-2225, https://doi.org/10.1016Zj.celrep.2018.02.003.

16. Zeisel A., Hochgerner H., Lönnerberg P., Johnsson A., Memic F., van der Zwan J., Häring M., Braun E., Borm L.E., La Manno G., Codeluppi S., Furlan A., Lee K., Skene N., Harris K.D., Hjerling-Leffler J., Arenas E., Ernfors P., Marklund U., Linnarsson S. Molecular architecture of the mouse nervous system. Cell 2018; 174(4): 999-1014.e22, https://doi.org/10.1016/j.cell.2018.06.021.

17. Russ D.E., Cross R.B.P., Li L., Koch S.C., Matson K.J.E., Yadav A., Alkaslasi M.R., Lee D.I., Le Pichon C.E., Menon V., Levine A.J. A harmonized atlas of mouse spinal cord cell types and their spatial organization. Nat Commun 2021; 12(1): 5722, https://doi.org/10.1038/s41467-021-25125-1.

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

18. Li Y., Chen Y., Li X., Wu J., Pan J.Y., Cai R.X., Yang R.Y., Wang X.D. RNA sequencing screening of differentially expressed genes after spinal cord injury. Neural Regen Res 2019; 14(9): 1583-1593, https://doi.org/10.4103/1673-5374.255994.

19. Zeisel A., Muñoz-Manchado A.B., Codeluppi S., Lönnerberg P., La Manno G., Juréus A., Marques S., Munguba H., He L., Betsholtz C., Rolny C., Castelo-Branco G., Hjerling-Leffler J., Linnarsson S. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015; 347(6226): 1138-1142, https://doi. org/10.1126/science.aaa1934.

20. Masuda T., Sankowski R., Staszewski O., Böttcher C., Amann L., Sagar S., Scheiwe C., Nessler S., Kunz P., van Loo G., Coenen V.A., Reinacher P.C., Michel A., Sure U., Gold R., Grün D., Priller J., Stadelmann C., Prinz M. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 2019; 566(7744): 388-392, https://doi.org/10.1038/s41586-019-0924-x.

21. Bomkamp C., Tripathy S.J., Bengtsson Gonzales C., Hjerling-Leffler J., Craig A.M., Pavlidis P. Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes. PLoS Comput Biol 2019; 15(6): e1007113, https://doi.org/ 10.1371/journal.pcbi.1007113.

22. Sagner A., Briscoe J. Establishing neuronal diversity in the spinal cord: a time and a place. Development 2019; 146(22): dev182154, https://doi.org/10.1242/dev.182154.

23. Ratz M., von Berlin L., Larsson L., Martin M., Westholm J.O., La Manno G., Lundeberg J., Frisén J. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat Neurosci 2022; 25(3): 285-294, https:// doi.org/10.1038/s41593-022-01011-x.

24. Stifani N. Motor neurons and the generation of spinal motor neuron diversity. Front Cell Neurosci 2014; 8: 293, https://doi.org/10.3389/fncel.2014.00293.

25. Morisaki Y., Niikura M., Watanabe M., Onishi K., Tanabe S., Moriwaki Y., Okuda T., Ohara S., Murayama S., Takao M., Uchida S., Yamanaka K., Misawa H. Selective expression of Osteopontin in ALS-resistant motor neurons is a critical determinant of late phase neurodegeneration mediated by matrix metalloproteinase-9. Sci Rep 2016; 6: 27354, https:// doi.org/10.1038/srep27354.

26. Bqczyk M., Manuel M., Roselli F., Zytnicki D. Diversity of mammalian motoneurons and motor units. Adv Neurobiol 2022; 28: 131-150, https://doi.org/10.1007/978-3-031-07167-6_6.

27. Miles G.B., Hartley R., Todd A.J., Brownstone R.M. Spinal cholinergic interneurons regulate the excitability of motoneurons during locomotion. Proc Natl Acad Sci USA 2007; 104(7): 2448-2453, https://doi.org/10.1073/ pnas.0611134104.

28. Bikoff J.B., Gabitto M.I., Rivard A.F., Drobac E.,

Machado T.A., Miri A., Brenner-Morton S., Famojure E., Diaz C., Alvarez F.J., Mentis G.Z., Jessell T.M. Spinal inhibitory interneuron diversity delineates variant motor microcircuits. Cell 2016; 165(1): 207-219, https://doi.org/10.1016/j. cell.2016.01.027.

29. Bikoff J.B. Interneuron diversity and function in the spinal motor system. Curr Opin Physiol 2019; 8: 36-43, https:// doi.org/10.1016/j.cophys.2018.12.013.

30. Chen S., Yang G., Zhu Y., Liu Z., Wang W., Wei J., Li K., Wu J., Chen Z., Li Y., Mu S., OuYang L., Lei W. A comparative study of three interneuron types in the rat spinal cord. PLoS One 2016; 11(9): e0162969, https://doi.org/10.1371/journal. pone.0162969.

31. Bertuzzi M., Ampatzis K. Spinal cholinergic interneurons differentially control motoneuron excitability and alter the locomotor network operational range. Sci Rep 2018; 8(1): 1988, https://doi.org/10.1038/s41598-018-20493-z.

32. Sweeney L.B., Bikoff J.B., Gabitto M.I., Brenner-Morton S., Baek M., Yang J.H., Tabak E.G., Dasen J.S., Kintner C.R., Jessell T.M. Origin and segmental diversity of spinal inhibitory interneurons. Neuron 2018; 97(2): 341-355.e3, https://doi.org/10.1016Zj.neuron.2017.12.029.

33. Deska-Gauthier D., Zhang Y. The functional diversity of spinal interneurons and locomotor control. Curr Opin Physiol 2019; 8: 99-108, https://doi.org/10.1016/j.cophys. 2019.01.005.

34. Laliberte A.M., Goltash S., Lalonde N.R., Bui T.V. Propriospinal neurons: essential elements of locomotor control in the intact and possibly the injured spinal cord. Front Cell Neurosci 2019; 13: 512, https://doi.org/10.3389/ fncel.2019.00512.

35. Nascimento F., Broadhead M.J., Tetringa E., Tsape E., Zagoraiou L., Miles G.B. Synaptic mechanisms underlying modulation of locomotor-related motoneuron output by premotor cholinergic interneurons. Elife 2020; 9: e54170, https://doi.org/10.7554/elife.54170.

36. Alkaslasi M.R., Piccus Z.E., Hareendran S., Silberberg H., Chen L., Zhang Y., Petros T.J., Le Pichon C.E. Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord. Nat Commun 2021; 12(1): 2471, https://doi.org/10.1038/s41467-021-22691-2.

37. Blum J.A., Klemm S., Shadrach J.L., Guttenplan K.A., Nakayama L., Kathiria A., Hoang P.T., Gautier O., Kaltschmidt J.A., Greenleaf W.J., Gitler A.D. Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons. Nat Neurosci 2021; 24(4): 572-583, https://doi.org/10.1038/ s41593-020-00795-0.

38. Liau E.S., Jin S., Chen Y.C., Liu W.S., Calon M., Nedelec S., Nie Q., Chen J.A. Single-cell transcriptomic analysis reveals diversity within mammalian spinal motor neurons. Nat Commun 2023; 14(1): 46, https://doi.org/10.1038/ s41467-022-35574-x.

39. Kaplan A., Spiller K.J., Towne C., Kanning K.C., Choe G.T., Geber A., Akay T., Aebischer P., Henderson C.E. Neuronal matrix metalloproteinase-9 is a determinant of selective neurodegeneration. Neuron 2014; 81(2): 333-348, https://doi.org/10.1016/j.neuron.2013.12.009.

40. Nijssen J., Comley L.H., Hedlund E. Motor neuron vulnerability and resistance in amyotrophic lateral sclerosis. Acta Neuropathol 2017; 133(6): 863-885, https://doi.org/ 10.1007/s00401-017-1708-8.

41. Schweingruber C., Hedlund E. The cell autonomous and non-cell autonomous aspects of neuronal vulnerability and resilience in amyotrophic lateral sclerosis. Biology (Basel) 2022; 11(8): 1191, https://doi.org/10.3390/biology11081191.

42. Taylor J.P., Brown R.H. Jr., Cleveland D.W. Decoding ALS: from genes to mechanism. Nature 2016; 539(7628): 197206, https://doi.org/10.1038/nature20413.

43. Shi L.L., Zhang N., Xie X.M., Chen Y.J., Wang R., Shen L., Zhou J.S., Hu J.G., Lü H.Z. Transcriptome profile of rat genes in injured spinal cord at different stages by RNA-sequencing. BMC Genomics 2017; 18(1): 173, https://doi. org/10.1186/s12864-017-3532-x.

44. Floriddia E.M., Lourengo T., Zhang S., van Bruggen D., Hilscher M.M., Kukanja P., Gongalves Dos Santos J.P., Altinkök M., Yokota C., Llorens-Bobadilla E., Mulinyawe S.B., Gräos M., Sun L.O., Frisen J., Nilsson M., Castelo-Branco G. Distinct oligodendrocyte populations have spatial preference and different responses to spinal cord injury. Nat Commun 2020; 11(1): 5860, https://doi.org/10.1038/s41467-020-19453-x.

45. Llorens-Bobadilla E., Chell J.M., Le Merre P., Wu Y., Zamboni M., Bergensträhle J., Stenudd M., Sopova E., Lundeberg J., Shupliakov O., Carlen M., Frisen J. A latent lineage potential in resident neural stem cells enables spinal cord repair. Science 2020; 370(6512): eabb8795, https://doi. org/10.1126/science.abb8795.

46. Poplawski G.H.D., Kawaguchi R., Van Niekerk E., Lu P., Mehta N., Canete P., Lie R., Dragatsis I., Meves J.M., Zheng B., Coppola G., Tuszynski M.H. Injured adult neurons regress to an embryonic transcriptional growth state. Nature 2020; 581(7806): 77-82, https://doi.org/10.1038/s41586-020-2200-5.

47. Milich L.M., Choi J.S., Ryan C., Cerqueira S.R., Benavides S., Yahn S.L., Tsoulfas P., Lee J.K. Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. J Exp Med 2021; 218(8): e20210040, https://doi.org/10.1084/jem.20210040.

48. Kathe C., Skinnider M.A., Hutson T.H., Regazzi N., Gautier M., Demesmaeker R., Komi S., Ceto S., James N.D., Cho N., Baud L., Galan K., Matson K.J.E., Rowald A., Kim K., Wang R., Minassian K., Prior J.O., Asboth L., Barraud Q., Lacour S.P., Levine A.J., Wagner F., Bloch J., Squair J.W., Courtine G. The neurons that restore walking after paralysis. Nature 2022; 611(7936): 540-547, https://doi.org/10.1038/ s41586-022-05385-7.

49. Matson K.J.E., Russ D.E., Kathe C., Hua I., Maric D., Ding Y., Krynitsky J., Pursley R., Sathyamurthy A., Squair J.W., Levi B.P., Courtine G., Levine A.J. Single cell atlas of spinal cord injury in mice reveals a pro-regenerative signature in spinocerebellar neurons. Nat Commun 2022; 13(1): 5628, https://doi.org/10.1038/s41467-022-33184-1.

50. Rodrigo Albors A., Singer G.A., Llorens-Bobadilla E., Frisen J., May A.P., Ponting C.P., Storey K.G. An ependymal cell census identifies heterogeneous and ongoing cell maturation in the adult mouse spinal cord that changes dynamically on injury. Dev Cell 2023; 58(3): 239-255.e10, https://doi.org/10.1016Zj.devcel.2023.01.003.

51. Yadav A., Matson K.J.E., Li L., Hua I., Petrescu J., Kang K., Alkaslasi M.R., Lee D.I., Hasan S., Galuta A., Dedek A., Ameri S., Parnell J., Alshardan M.M., Qumqumji F.A., Alhamad S.M., Wang A.P., Poulen G., Lonjon N., Vachiery-Lahaye F., Gaur P., Nalls M.A., Qi Y.A., Maric D., Ward M.E., Hildebrand M.E., Mery P.F., Bourinet E., Bauchet L., Tsai E.C.,

Phatnani H., Le Pichón C.E., Menon V., Levine A.J. A cellular taxonomy of the adult human spinal cord. Neuron 2023; 111(3): 328-344.e7, https://doi.org/10.1016/j.neuron.2023.01.007.

52. Sun J., Qiu J., Yang Q., Ju Q., Qu R., Wang X., Wu L., Xing L. Single-cell RNA sequencing reveals dysregulation of spinal cord cell types in a severe spinal muscular atrophy mouse model. PLoS Genet 2022; 18(9): e1010392, https://doi. org/10.1371/journal.pgen.1010392.

53. Chelyshev Y. More attention on segments remote from the primary spinal cord lesion site. Front Biosci (Landmark Ed) 2022; 27(8): 235, https://doi.org/10.31083/jfbl2708235.

54. Chelyshev Y.A., Shaymardanova G.F., Muhamedshina Y.O., Nigmetzyanova M.V. Glial barriers at spinal cord injury as a target of gene-cell therapy. Nevrologiceskij vestnik 2013; 45(1): 87-93.

55. Northcutt A.J., Schulz D.J. Molecular mechanisms of homeostatic plasticity in central pattern generator networks. Dev Neurobiol 2020; 80(1-2): 58-69, https://doi.org/10.1002/ dneu.22727.

56. Grillner S., Kozlov A. The CPGs for limbed locomotion-facts and Action. Int J Mol Sci 2021; 22(11): 5882, https://doi. org/10.3390/ijms22115882.

57. Fink K.L., Cafferty W.B. Reorganization of intact descending motor circuits to replace lost connections after injury. Neurotherapeutics 2016; 13(2): 370-381, https://doi. org/10.1007/s13311-016-0422-x.

58. Wang Y., Wu W., Wu X., Sun Y., Zhang Y.P., Deng L.X., Walker M.J., Qu W., Chen C., Liu N.K., Han Q., Dai H., Shields L.B., Shields C.B., Sengelaub D.R., Jones K.J., Smith G.M., Xu X.M. Remodeling of lumbar motor circuitry remote to a thoracic spinal cord injury promotes locomotor recovery. Elife 2018; 7: e39016, https://doi.org/10.7554/elife. 39016.

59. Anderson M.A., Squair J.W., Gautier M., Hutson T.H., Kathe C., Barraud Q., Bloch J., Courtine G. Natural and targeted circuit reorganization after spinal cord injury. Nat Neurosci 2022; 25(12): 1584-1596, https://doi.org/10.1038/ s41593-022-01196-1.

60. McBride R.L., Feringa E.R. Ventral horn motoneurons 10, 20 and 52 weeks after T-9 spinal cord transection. Brain Res Bull 1992; 28(1): 57-60, https://doi.org/10.1016/0361-9230(92)90230-u.

61. Yokota K., Kubota K., Kobayakawa K., Saito T., Hara M., Kijima K., Maeda T., Katoh H., Ohkawa Y., Nakashima Y., Okada S. Pathological changes of distal motor neurons after complete spinal cord injury. Mol Brain 2019; 12(1): 4, https:// doi.org/10.1186/s13041-018-0422-3.

62. Poplawski G.H., Tuszynski M.H. Regeneration of corticospinal axons into neural progenitor cell grafts after spinal cord injury. Neurosci Insights 2020; 15: 2633105520974000, https://doi.org/10.1177/2633105520974000.

63. Jing X., Wang T., Huang S., Glorioso J.C., Albers K.M. The transcription factor Sox11 promotes nerve regeneration through activation of the regeneration-associated gene Sprr1a. Exp Neurol 2012; 233(1): 221-232, https://doi.org/10.1016/j. expneurol.2011.10.005.

64. Fagoe N.D., Attwell C.L., Kouwenhoven D., Verhaagen J., Mason M.R. Overexpression of ATF3 or the combination of ATF3, c-Jun, STAT3 and Smad1 promotes regeneration of the central axon branch of sensory neurons but without synergistic effects. Hum Mol Genet 2015; 24(23): 6788-6800, https://doi.org/10.1093/hmg/ddv383.

65. Nguyen M.Q., Le Pichon C.E., Ryba N. Stereotyped

transcriptomic transformation of somatosensory neurons in response to injury. Elife 2019; 8: e49679, https://doi. org/10.7554/eLife.49679.

66. Renthal W., Tochitsky I., Yang L., Cheng Y.C., Li E., Kawaguchi R., Geschwind D.H., Woolf C.J. Transcriptional reprogramming of distinct peripheral sensory neuron subtypes after axonal injury. Neuron 2020; 108(1): 128-144.e9, https:// doi.org/10.1016/j.neuron.2020.07.026.

67. Akram R., Anwar H., Javed M.S., Rasul A., Imran A., Malik S.A., Raza C., Khan I.U., Sajid F., Iman T., Sun T., Han H.S., Hussain G. Axonal regeneration: underlying molecular mechanisms and potential therapeutic targets. Biomedicines 2022; 10(12): 3186, https://doi.org/10.3390/ biomedicines10123186.

68. Westergard T., Rothstein J.D. Astrocyte diversity: current insights and future directions. Neurochem Res 2020; 45(6): 1298-1305, https://doi.org/10.1007/s11064-020-02959-7.

69. Escartin C., Galea E., Lakatos A., O'Callaghan J.P., Petzold G.C., Serrano-Pozo A., Steinhäuser C., Volterra A., Carmignoto G., Agarwal A., Allen N.J., Araque A., Barbeito L., Barzilai A., Bergles D.E., Bonvento G., Butt A.M., Chen W.T., Cohen-Salmon M., Cunningham C., Deneen B., De Strooper B., Díaz-Castro B., Farina C., Freeman M., Gallo V., Goldman J.E., Goldman S.A., Götz M., Gutiérrez A., Haydon P.G., Heiland D.H., Hol E.M., Holt M.G., Iino M., Kastanenka K.V., Kettenmann H., Khakh B.S., Koizumi S., Lee C.J., Liddelow S.A., MacVicar B.A., Magistretti P., Messing A., Mishra A., Molofsky A.V., Murai K.K., Norris C.M., Okada S., Oliet S.H.R., Oliveira J.F., Panatier A., Parpura V., Pekna M., Pekny M., Pellerin L., Perea G., Pérez-Nievas B.G., Pfrieger F.W., Poskanzer K.E., Quintana F.J., Ransohoff R.M., Riquelme-Perez M., Robel S., Rose C.R., Rothstein J.D., Rouach N., Rowitch D.H., Semyanov A., Sirko S., Sontheimer H., Swanson R.A., Vitorica J., Wanner I.B., Wood L.B., Wu J., Zheng B., Zimmer E.R., Zorec R., Sofroniew M.V., Verkhratsky A. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci 2021; 24(3): 312-325, https://doi.org/10.1038/s41593-020-00783-4.

70. Endo F., Kasai A., Soto J.S., Yu X., Qu Z., Hashimoto H., Gradinaru V., Kawaguchi R., Khakh B.S. Molecular basis of astrocyte diversity and morphology across the CNS in health and disease. Science 2022; 378(6619): eadc9020, https://doi. org/10.1126/science.adc9020.

71. Prabhakar P., Pielot R., Landgraf P., Wissing J., Bayrhammer A., van Ham M., Gundelfinger E.D., Jänsch L., Dieterich D.C., Müller A. Monitoring regional astrocyte diversity by cell type-specific proteomic labeling in vivo. Glia 2023; 71(3): 682-703, https://doi.org/10.1002/glia.24304.

72. Wei H., Wu X., Withrow J., Cuevas-Diaz Duran R., Singh S., Chaboub L.S., Rakshit J., Mejia J., Rolfe A., Herrera J.J., Horner P.J., Wu J.Q. Glial progenitor heterogeneity and key regulators revealed by single-cell RNA sequencing provide insight to regeneration in spinal cord injury. Cell Rep 2023; 42(5): 112486, https://doi.org/10.1016/j. celrep.2023.112486.

73. Bernal A., Arranz L. Nestin-expressing progenitor cells: function, identity and therapeutic implications. Cell Mol Life Sci 2018; 75(12): 2177-2195, https://doi.org/10.1007/s00018-018-2794-z.

74. Adams K.L., Gallo V. The diversity and disparity of the glial scar. Nat Neurosci 2018; 21(1): 9-15, https://doi. org/10.1038/s41593-017-0033-9.

75. Nicaise A.M., D'Angelo A., lonescu R.B., Krzak G., Willis C.M., Pluchino S. The role of neural stem cells in regulating glial scar formation and repair. Cell Tissue Res 2022; 387(3): 399-414, https://doi.org/10.1007/s00441-021-03554-0.

76. Alfaro-Cervello C., Cebrian-Silla A., Soriano-Navarro M., Garcia-Tarraga P., Matías-Guiu J., Gomez-Pinedo U., Molina Aguilar P., Alvarez-Buylla A., Luquin M.R., Garcia-Verdugo J.M. The adult macaque spinal cord central canal zone contains proliferative cells and closely resembles the human. J Comp Neurol 2014; 522(8): 1800-1817, https://doi. org/10.1002/cne.23501.

77. Paniagua-Torija B., Norenberg M., Arevalo-M artin A., Carballosa-Gautam M.M., Campos-Martin Y., Molina-Holgado E., Garcia-Ovejero D. Cells in the adult human spinal cord ependymal region do not proliferate after injury. J Pathol 2018; 246(4): 415-421, https://doi.org/10.1002/path.5151.

78. Dromard C., Guillon H., Rigau V., Ripoll C., Sabourin J.C., Perrin RE., Scamps F., Bozza S., Sabatier P., Lonjon N., Duffau H., Vachiery-Lahaye F., Prieto M., Tran Van Ba C., Deleyrolle L., Boularan A., Langley K., Gaviria M., Privat A., Hugnot J.P., Bauchet L. Adult human spinal cord harbors neural precursor cells that generate neurons and glial cells in vitro. J Neurosci Res 2008; 86(9): 1916-1926, https://doi.org/10.1002/jnr.21646.

79. Mothe A.J., Zahir T., Santaguida C., Cook D., Tator C.H. Neural stem/progenitor cells from the adult human spinal cord are multipotent and self-renewing and differentiate after transplantation. PLoS One 2011; 6(11): e27079, https://doi. org/10.1371/journal.pone.0027079.

80. Hugnot J.P. Isolate and culture neural stem cells from the mouse adult spinal cord. Methods Mol Biol 2013; 1059: 5363, https://doi.org/10.1007/978-1-62703-574-3_5.

81. Frederico B., Martins I., Chapela D., Gasparrini F., Chakravarty P., Ackels T., Piot C., Almeida B., Carvalho J., Ciccarelli A., Peddie C.J., Rogers N., Briscoe J., Guillemot F., Schaefer A.T., Saúde L., Reis e Sousa C. DNGR-1-tracing marks an ependymal cell subset with damage-responsive neural stem cell potential. Dev Cell 2022; 57(16): 1957-1975.e9, https://doi.org/10.1016/j.devcel.2022.07.012.

82. Stenudd M., Sabelström H., Llorens-Bobadilla E., Zamboni M., Blom H., Brismar H., Zhang S., Basak O., Clevers H., Göritz C., Barnabé-Heider F., Frisén J. Identification of a discrete subpopulation of spinal cord ependymal cells with neural stem cell properties. Cell Rep 2022; 38(9): 110440, https://doi.org/10.1016/j.celrep.2022.110440.

83. Fiorelli R., Cebrian-Silla A., Garcia-Verdugo J.M., Raineteau O. The adult spinal cord harbors a population of GFAP-positive progenitors with limited self-renewal potential. Glia 2013; 61(12): 2100-2113, https://doi.org/10.1002/glia.22579.

84. Redmond S.A., Figueres-Oñate M., Obernier K., Nascimento M.A., Parraguez J.I., López-Mascaraque L., Fuentealba L.C., Alvarez-Buylla A. Development of ependymal and postnatal neural stem cells and their origin from a common embryonic progenitor. Cell Rep 2019; 27(2): 429-441.e3, https://doi.org/10.1016/j.celrep.2019.01.088.

85. Meletis K., Barnabé-Heider F., Carlén M., Evergren E., Tomilin N., Shupliakov O., Frisén J. Spinal cord injury reveals multilineage differentiation of ependymal cells. PLoS Biol 2008; 6(7): e182, https://doi.org/10.1371/journal.pbio.0060182.

86. Rolls A., Shechter R., Schwartz M. The bright side of the glial scar in CNS repair. Nat Rev Neurosci 2009; 10(3): 235-241, https://doi.org/10.1038/nrn2591.

87. Barnabe-Heider F., Göritz C., Sabelström H., Takebayashi H., Pfrieger F.W., Meletis K., Frisen J. Origin of new glial cells in intact and injured adult spinal cord. Cell Stem Cell 2010; 7(4): 470-482, https://doi.org/10.1016/j.stem. 2010.07.014.

88. Marinelli S., Basilico B., Marrone M.C., Ragozzino D. Microglia-neuron crosstalk: signaling mechanism and control of synaptic transmission. Semin Cell Dev Biol 2019; 94: 138-151, https://doi.org/10.1016/j.semcdb.2019.05.017.

89. Borst K., Dumas A.A., Prinz M. Microglia: immune and non-immune functions. Immunity 2021; 54(10): 2194-2208, https://doi.org/10.1016/j.immuni.2021.09.014.

90. Ball J.B., Green-Fulgham S.M., Watkins L.R. Mechanisms of microglia-mediated synapse turnover and synaptogenesis. Prog Neurobiol 2022; 218: 102336, https:// doi.org/10.1016/j.pneurobio.2022.102336.

91. McNamara N.B., Munro D.A.D., Bestard-Cuche N., Uyeda A., Bogie J.F.J., Hoffmann A., Holloway R.K., Molina-Gonzalez I., Askew K.E., Mitchell S., Mungall W., Dodds M., Dittmayer C., Moss J., Rose J., Szymkowiak S., Amann L., McColl B.W., Prinz M., Spires-Jones T.L., Stenzel W., Horsburgh K., Hendriks J.J.A., Pridans C., Muramatsu R., Williams A., Priller J., Miron V.E. Microglia regulate central nervous system myelin growth and integrity. Nature 2023; 613(7942): 120-129, https://doi.org/10.1038/ s41586-022-05534-y.

92. Li Q., Cheng Z., Zhou L., Darmanis S., Neff N.F., Okamoto J., Gulati G., Bennett M.L., Sun L.O., Clarke L.E., Marschallinger J., Yu G., Quake S.R., Wyss-Coray T., Barres B.A. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 2019; 101(2): 207-223.e10, https://doi. org/10.1016/j.neuron.2018.12.006.

93. Hakim R., Zachariadis V., Sankavaram S.R., Han J., Harris R.A., Brundin L., Enge M., Svensson M. Spinal cord injury induces permanent reprogramming of microglia into a disease-associated state which contributes to functional recovery. J Neurosci 2021; 41(40): 8441-8459, https://doi. org/10.1523/jneurosci.0860-21.2021.

94. Healy L.M., Zia S., Plemel J.R. Towards a definition of microglia heterogeneity. Commun Biol 2022; 5(1): 1114, https:// doi.org/10.1038/s42003-022-04081-6.

95. Xu L., Wang J., Ding Y., Wang L., Zhu Y.J. Current knowledge of microglia in traumatic spinal cord injury. Front Neurol 2022; 12: 796704, https://doi.org/10.3389/ fneur.2021.796704.

96. Hickman S., Izzy S., Sen P., Morsett L., El Khoury J. Microglia in neurodegeneration. Nat Neurosci 2018; 21(10): 1359-1369, https://doi.org/10.1038/s41593-018-0242-x.

97. Zhou X., He X., Ren Y. Function of microglia and macrophages in secondary damage after spinal cord injury. Neural Regen Res 2014; 9(20): 1787-1795, https://doi. org/10.4103/1673-5374.143423.

98. Kroner A., Rosas Almanza J. Role of microglia in spinal cord injury. Neurosci Lett 2019; 709: 134370, https://doi. org/10.1016/j.neulet.2019.134370.

99. Ding Y., Zhang D., Wang S., Zhang X., Yang J. Hematogenous macrophages: a new therapeutic target for spinal cord injury. Front Cell Dev Biol 2021; 9: 767888, https:// doi.org/10.3389/fcell.2021.767888.

100. Kisucka A., Bimbova K., Bacova M., Galik J., Lukacova N. Activation of neuroprotective microglia and astrocytes at the lesion site and in the adjacent segments

is crucial for spontaneous locomotor recovery after spinal cord injury. Cells 2021; 10(8): 1943, https://doi.org/10.3390/ cells10081943.

101. Kierdorf K., Masuda T., Jordâo M.J.C., Prinz M. Macrophages at CNS interfaces: ontogeny and function in health and disease. Nat Rev Neurosci 2019; 20(9): 547-562, https://doi.org/10.1038/s41583-019-0201-x.

102. Brennan F.H., Li Y., Wang C., Ma A., Guo Q., Li Y., Pukos N., Campbell W.A., Witcher K.G., Guan Z., Kigerl K.A., Hall J.C.E., Godbout J.P., Fischer A.J., McTigue D.M., He Z., Ma Q., Popovich P.G. Microglia coordinate cellular interactions during spinal cord repair in mice. Nat Commun

2022; 13(1): 4096, https://doi.org/10.1038/s41467-022-31797-0.

103. Li C., Wu Z., Zhou L., Shao J., Hu X., Xu W., Ren Y., Zhu X., Ge W., Zhang K., Liu J., Huang R., Yu J., Luo D., Yang X., Zhu W., Zhu R., Zheng C., Sun Y.E., Cheng L. Temporal and spatial cellular and molecular pathological alterations with single-cell resolution in the adult spinal cord after injury. Signal Transduct Target Ther 2022; 7(1): 65, https://doi.org/10.1038/s41392-022-00885-4.

104. Yan L., Fu J., Dong X., Chen B., Hong H., Cui Z. Identification of hub genes in the subacute spinal cord injury in rats. BMC Neurosci 2022; 23(1): 51, https://doi.org/10.1186/ s12868-022-00737-5.

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