Human Pain Atlas for Sensory Neurons

Chronic pain remains a major challenge for modern healthcare, with limited effective treatments. Neuropathic pain is linked to increased activity in pain-relevant subgroups of the sensory neurons located in the dorsal root ganglia. One of the key reasons for the lack of effective treatments is the incomplete understanding of the neurobiological mechanisms underlying chronic neuropathic pain, particularly in the context of human-specific pain pathways. While animal models have provided valuable insights, they do not fully recapitulate human pain experiences due to significant species differences, especially in the structure and function of sensory neurons, which has led to significant translational hurdles in the development of new pain therapies.

In recent years, research has made significant strides in creating transcriptomic atlases of sensory neurons. These atlases provide valuable insights into the genetic and molecular landscape of human nociceptors. However, they are often limited by the inability to fully link these molecular findings with functional data, especially in the context of chronic pain. Additionally, many of these atlases are based on limited data from healthy donors, thus not fully capturing the heterogeneity found in pain-related sensory neurons. While recent efforts have begun to address these gaps by exploring the relationship between transcriptomic data and sensory neuron function, the field still faces major limitations. One key issue is the identification of pain-related sensory neurons, which can exhibit diverse activity patterns that may not align with current classifications. Moreover, existing atlases lack the integration of long-read RNA sequencing, which is crucial for identifying splice variants and other molecular nuances that may be critical for understanding pain pathways.

This consortium will leverage state-of-the-art technologies like Patch-Seq, single-cell long-read RNA sequencing, and microneurography to build a comprehensive atlas of human sensory neurons involved in pain processing. This approach will integrate high-resolution transcriptomic data with functional information, which is essential for addressing the current gap in our understanding of pain sensory neurons. One of the main challenges in the field has been the difficulty of linking molecular data to function, particularly when it comes to pain subtypes. The proposed project's approach will address this challenge by incorporating multiple techniques, which are expected to produce a more nuanced and clinically relevant understanding of sensory neuron diversity. Despite the promising progress made in creating human Dorsal Root Ganglia (DRG) atlases, the identification of pain-related neurons remains a significant challenge. There are concerns that certain pain-related neurons may not be captured using standard stimulation protocols. This is especially true for neurons whose activity patterns differ from known models. To address this, the proposal will combine detailed in-vitro and in-vivo electrophysiological recordings with advanced RNA sequencing to characterise sensory neurons, using stimulation protocols that are tailored to capture a wider range of neuronal subtypes. We will develop subtype-specific stimulation paradigms for in vivo microneurography as another innovative aspect of this proposal. This method could dramatically enhance our ability to stratify patients based on the specific sensory neuron subtypes contributing to their pain, thus facilitating personalised approaches to pain management. Moreover, integrating human tissue samples from both healthy and chronic pain patients is a significant step forward in ensuring that findings are potentially validated in a clinical scenario. This approach not only makes the project more clinically relevant but also enhances its translational potential since there is still a huge demand for novel pharmacological targets for pain treatment. With our consortium, we expect to identify novel sensory neuron-subtype specific transcript isoforms as biomarkers and druggable targets for e.g. gene therapeutic approaches like ASOs (antisense-oligonucleotides). By selectively targeting transcript isoforms that are specific to sensory neurons of chronic pain patients, ASO-mediated splice modulation that could change the amount of a "pathological" transcript isoform will be investigated in future follow-up studies as "genetic pain killers".

This project aims to fill the critical gap in the current understanding of pain-related sensory neurons by generating a comprehensive, multidimensional atlas of human sensory neurons. By combining the latest advances in transcriptomics, electrophysiology, and microneurography, the proposal promises to offer new insights into the molecular and functional diversity of sensory neurons involved in chronic pain. This integrated approach not only addresses existing knowledge gaps but also provides a solid foundation for future therapeutic advancements in pain management.

The main objective is to elucidate the molecular and functional mechanisms underlying chronic neuropathic pain by building the first comprehensive, multimodal sensory neuron atlas based on human DRG material. This dataset will integrate electrophysiology, gene expression (including long-read single-cell RNA sequencing), and in vivo microneurography data in humans, ultimately identifying novel molecular and electrophysiological biomarkers and neuronal subtypes involved in pain processing.

The UK organisation will be responsible for obtaining microneurography recordings from human participants, including patients suffering from chronic pain conditions and healthy volunteers, to refine neuron subtype classifications and develop new stimulation protocols. These will be used for refinement of the functional characterisation of neurons in the Patch-Seq Pipeline. This will end in a multimodal atlas of sensory neurons, with integration of long-read transcriptomic data with electrophysiological properties from Patch-seq and in vivo microneurography to classify pain-relevant neuronal subtypes.