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New Review on the Multifaceted Roles of CD47! 🎉

Looking for some summer reading? Then I suggest the latest review on CD47, a receptor that is gaining increasing recognition for its versatile functions beyond its well-known role in preventing macrophage-mediated phagocytosis: https://www.nature.com/articles/s41418-024-01347-w

Published this July in Cell Death & Differentiation, this review delves deep into the emerging noncanonical roles of CD47, revealing its critical involvement in regulating processes such as proliferation, migration, apoptosis, differentiation, stress responses, and metabolism.

Key highlights from the review include:

• An in-depth exploration of the cell-intrinsic signalling pathways regulated by CD47 across diverse cell types and environments.
• Detailed discussion on pivotal pathways like PI3K/AKT, MAPK/ERK, and nitric oxide signalling, along with those involved in glucose, lipid, and mitochondrial metabolism.
• Insights into how CD47’s interactions are often cell- and context-specific, adding layers of regulatory complexity.
• The importance of these pathways in maintaining tissue homeostasis and the implications for understanding CD47’s role in various cancers.

Understanding these pathways not only broadens our knowledge of cellular processes but also paves the way for the development of innovative CD47-targeted therapies, especially given its dysregulation in numerous cancers.

Well done to the authors – @Ruhi Polara, @Raja Ganesan, @Stuart M. Pitson and @Nirmal Robinson – on their success! This review is sure to be an invaluable resource for researchers and clinicians alike.

#CD47 #CellBiology #CellSignaling #Review #InsightEditingLondon

Understanding the Role of IFN-γ in Preventing Melanoma Spread

Cancer metastasis remains one of the most daunting challenges in oncology. For cancers such as melanoma, the spread of tumour cells to distant parts of the body significantly complicates treatment and worsens patient prognosis. A critical element in this spread is the lymphatic system, which can act as a highway for tumour cells. Recent research from our colleagues in Zhengzhou, China, now sheds light on how the immune signalling molecule IFN-γ has a crucial role in maintaining the integrity of the lymphatic system, thereby preventing the dissemination of melanoma cells.

Key Takeaways

1. Lymphatic System and Cancer Spread:
   • The lymphatic system is essential in preventing the spread of tumour cells, such as melanoma.
   • Tumour-associated lymphatic vessel barrier function is vital in stopping cancer cells from migrating to distant sites.
2. Role of IFN-γ:
   • IFN-γ is known to inhibit lymphangiogenesis, a process linked to cancer metastasis.
   • This study highlights IFN-γ’s role in regulating the barrier function of lymphatic vessels, thereby influencing cancer cell dissemination.
3. Mechanisms of IFN-γ Action:
   • Using lymphatic endothelial cell (LEC)-specific IFN-γ receptor knockout mice, Linyu Zhu et al., found that the absence of IFN-γ receptors increased melanoma cell spread to lymph nodes.
   • IFN-γ inhibits the migration of melanoma cells across LECs, enhancing the lymphatic barrier.
4. Tight Junction Proteins:
   • IFN-γ upregulates Claudin-3, a tight junction protein in LECs.
   • Knocking down Claudin-3 in LECs negated the inhibitory effect of IFN-γ on melanoma cell migration, emphasizing Claudin-3’s role in maintaining barrier integrity.
5. Metabolic Pathways:
   • IFN-γ inhibits AMPK signalling activation, a pathway involved in fatty acid metabolism regulation.
   • Altering fatty acid metabolism and AMPK activation in LECs affected melanoma cell spread, linking metabolic processes to lymphatic barrier function.

Why This Matters

Understanding the mechanisms behind cancer metastasis is crucial for developing effective treatments. The insights from this study highlight the importance of IFN-γ in maintaining lymphatic vessel integrity and preventing cancer spread. By unravelling how IFN-γ influences tight junction proteins and metabolic pathways in LECs, researchers can explore new therapeutic strategies to enhance lymphatic barrier function and inhibit metastasis.

 

To dive deeper into the findings and explore the detailed mechanisms, read the full article and stay informed about the latest advancements in cancer metastasis research:

https://www.sciencedirect.com/science/article/abs/pii/S0925443924003077?via%3Dihub

 

Many congratulations to all those involved in this inspiring study: it was a pleasure working with you!

Revolutionizing Cancer Diagnosis: How Deep Learning and Better Labelling Are Changing the Game

In the ever-evolving landscape of cancer research and treatment, technological advancements continue to push boundaries and offer new hope. A recent study published last month in Frontiers in Immunology delves into a particularly promising area: the use of deep learning (DL) models to predict biomarker expression in images of haematoxylin and eosin (H&E)-stained tissues. This advancement could vastly improve access to critical immunophenotyping, essential for monitoring therapy, discovering new biomarkers, and developing personalized treatments. But there’s a catch—how we derive the data that trains these models significantly impacts their performance.

The Study: Unveiling the Methodology

Our great colleagues in Singapore devised a study to tackle this important aspect of DL models—namely, how the derivation of ground truth cell labels affects their predictive accuracy. The study was spearheaded by Joe Yeong, Mai Chan Lau and Yiyu Cai et al., and focused on CD3+ T-cells in lung cancer tissues. The researchers compared two approaches using Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining models:

1. Same-Section Model: This model was trained with cell labels obtained from the exact same tissue section as the H&E-stained section.
2. Serial-Section Model: This one used cell labels derived from an adjacent tissue section, which is the conventional method.

Key Findings: A Step Forward in Precision

The results were clear and compelling. The same-section model outperformed the serial-section model in several significant ways:

• Improved Prediction Performance: The accuracy of predicting biomarker expression was markedly better in the same-section model.
• Better Patient Stratification: When applied to a public lung cancer cohort, the same-section model more effectively stratified patients based on survival outcomes.

These findings suggest that using ground truth cell labels from the same tissue section enhances the accuracy and clinical utility of DL models in immunophenotyping.

Why Should You Care?

This study isn’t just a technical improvement; it’s a potential game-changer for patients and healthcare providers. Here’s why:

1. Enhanced Diagnostic Accuracy: More accurate predictions of biomarker expressions mean better diagnostic precision, leading to more targeted and effective treatments.
2. Personalized Treatment Plans: Improved stratification of patients can lead to more personalized treatment strategies, which are critical in managing complex diseases like cancer.
3. Accelerated Research and Discovery: Better DL models can expedite the discovery of new biomarkers, opening doors to novel therapeutic avenues.

Bridging the Gap in Cancer Care

In essence, this study underscores the importance of methodological rigor in the development of AI tools in healthcare. As we continue to refine these technologies, the promise of a future where personalized, effective cancer treatment is the norm rather than the exception becomes increasingly tangible.

We are really excited to see how this work progresses, and to explore the incredible advancements at the intersection of technology and healthcare, and how they’re transforming lives every day. It was such a pleasure to work with the study authors in preparing their submission and look forward to learning of their continued success: Congratulations to all those involved!

You can check out the full text here: Frontiers | Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy (frontiersin.org), available now at Frontiers in Immunology.

Many congratulations to the study authors – it was a pleasure working with you and learning about this exciting research!

A collaborative team of researchers based at the Shanghai Institute of Immunology, the First affiliated hospital of Zhejiang University, the Singapore Immunology Network, the National University of Singapore (NUS), and the SingHealth Duke-NUS Academic Medical Center, have published the results of a superb study into how mutations in CSF-1R affect #microglia to promote #neurodegenerative disease.

The focus of this study was hereditary diffuse leukoencephalopathy with axonal spheroids (#HDLS) – a rare yet fatal neurodegenerative disease underpinned by mutations in CSF-IR. Wei Jie Wong and colleagues developed a state-of-the-art macrophage and forebrain #organoid co-culture derived from induced pluripotent stem cells isolated from two patients with HDLS.

Compared to control organoids (in which CSF-1R gene mutations were corrected), they found that the patient-derived macrophages exhibited a metabolic shift towards the glycolytic pathway and reduced CSF-1 sensitivity. The result of this shift was an increase in IL-1β production and an activated inflammatory phenotype. RNA sequencing revealed that these macrophages existed in a reactive state, which led to impaired neuronal cell regulation.

This ground breaking study has provided yet more evidence of the diverse roles of microglia, as well as great insight into the pathological mechanisms of HDLS. We have no doubt that immunologists, neuroscientists, and clinicians will all be thrilled to read this exciting study and learn how Wei Jie Wong et al. tackled this complex question.

If you are interested to learn more, the paper is now available to download here, complete with referee reports: https://lnkd.in/dcY2kYxh

Many congratulations to the whole team: Wei Jie Wong, Yi Wen Zhu, Hai Ting Wang, Jia Wen Qian, Ziyi Li, Li Song, Zhao Yuan Liu, Wei Guo, Shuang Yan Zhang, Bing Su, Fang Ping He, Kang Wang and Florent Ginhoux!