MOSAIK is the first end-to-end Python workflow that integrates raw spatial transcriptomics data from both CosMx and Xenium platforms into a unified spatialdata format, enabling seamless quality control and downstream analyses. The toolkit addresses critical challenges in the field by supporting advanced analyses including re-segmentation for accurate transcript assignment, cell typing, tissue domain identification, and cell-cell communication. 
LINK: https://joss.theoj.org/papers/10.21105/joss.08795

Congratulations to Isobelle on winning Best Oral Presentation at the UK Interdisciplinary Breast Cancer Symposium for her talk entitled:

Spatial Transcriptomics Delineates Tumour Heterogeneity in NACT Triple-Negative Breast Cancer

Last week we had the great pleasure of discussing our research at the Cognizant Outreach event in support of #TechInPink2023 by Estée Lauder, attended by over 900 Cognizant associates around the world. Anita, Gregory , Mengyuan and Holly revealed more about the team’s use of AI to combat breast cancer in a panel and in discussion with attendees after the event.

Over 3,500 Cognizant associates from 27 countries have been hard at work as volunteers in the last 2 months, engaging with our Node Code Breakers Zooniverse project to annotate germinal centres in lymph nodes. They have generated an astonishing 80,000+ annotations for us! This phenomenal achievement will go a long way to helping us improve the accuracy of our AI models that support the pathological diagnoses of breast cancer patients.

We were absolutely blown away by the passion and dedication of everyone involved and would like to say a huge thank you to everyone at Cognizant for your tremendous efforts. 

And, as always, none of what we do could happen without the ongoing support of Breast Cancer Now .

If you would like to get involved in helping us tackle breast cancer then please do have a look at the Node Code Breakers site:

https://www.zooniverse.org/projects/effeli/node-code-breakers-looking-for-patterns-in-lymph-nodes

We’re seeking a highly motivated Research Associate with expertise in deep learning and spatial biology within the field of translational cancer research. The project will entail algorithm development and deployment of deep learning approaches for histomorphological characterisation of triple negative breast cancers. Through spatial transcriptomics, diverse transcriptional profiles will be mapped to regions of interest to decipher immune cell population dynamics in triple negative breast cancers. Subclonal and clonal T cell dynamics in these samples can be further investigated using TCR-sequencing.

This applicant will join the multidisciplinary Cancer Bioinformatics group (https://cancerbioinformatics.co.uk/) within the Breast Cancer Now Research Unit at King’s College London whose collective aims are to implement computational approaches for the integrated multi-modal analyses from model systems and patient studies of triple-negative breast cancers. We lead on the latest technologies to perform subcellular spatial transcriptomics and hold a wealth of whole slide image data for digital pathology approaches.

For more detailed information, please look at https://www.kcl.ac.uk/jobs/068345-research-associate-cancer-computer-scientist.

Today the Journal of Pathology published our work on developing a multiscale deep learning framework to predict how likely a patient is to develop secondary (incurable) breast cancer based on immune responses in the lymph nodes.

Congratulations to Gregory and Mengyuan for this excellent accomplishment.

KCL have featured the paper on the news site: https://www.kcl.ac.uk/news/scientists-ai-model-predict-breast-cancer-spread

Read the full paper here: https://pathsocjournals.onlinelibrary.wiley.com/doi/full/10.1002/path.6088

Congratulations Dr Ellie Alberts! We are so proud of you!

We are looking forward to sharing our latest research today at our inaugural Spatial Biology and Imaging Symposium. 

This week the team published a review on B Cells in Breast Cancer Pathology in a special issue of Cancers: B Cells and Antibodies in Immune Surveillance and Therapy for Cancer.

Congratulations in particular to Mengyuan, Ellie, Matthew and Vickie.