TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

Abstract

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making

“…By detecting patterns and anomalies in the data that human doctors may miss, AI can inform clinical decision-making and suggest potential treatments. Additionally, AI can predict the likelihood of a patient developing a particular disease or condition, allowing doctors to take preventive measures to reduce the risk of future complications [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

“…For example, FIB-SEM based ML in the freshwater sponge Spongilla lacustri was used to showcase a rendered 3D volume of the choanocyte chamber [16]. AI can automate tracking and counting of whole cells [17][18], cilia and other tubular structures [19][20] in (e.g., confocal) microscopy image slices and Z-stacks. In the field of optics, AI is being used to improve and develop novel medical imaging technologies with enhanced capabilities to diagnose and treat diseases.…”

Intercellular crosstalk mediated by tunneling nanotubes between central nervous system cells. What we need to advance

“…More recently, a machine learning approach potentially useful for TNT quantification was developed by Smirnov and colleagues to study the dynamics of dendritic spines using live-cell microscopy data ( Smirnov et al, 2018 ). A deep-learning artificial intelligence (AI) approach was first proposed by Ceran and colleagues for TNT analysis ( Ceran et al, 2022 ). All these methods were able to identify only 50%–60% of human expert-identified TNTs.…”

Tunneling nanotubes and tumor microtubes—Emerging data on their roles in intercellular communication and pathophysiology: Summary of an International FASEB Catalyst Conference October 2023

“…49 Finally, the introduction of deep learning methods helped bring advances in artificial intelligence to play in ongoing work to automate the detection and quantification of TNTs and connected cells. 50 Next, Dr. Chiara Zurzolo from the Institut Pasteur in Paris, France presented her work on the potential for TNTs to reshape brain connectivity, and their potential role and progression of neurodegenerative diseases in a presentation entitled "Tunneling Nanotubes, reshaping brain connectivity and role in the progression of neurodegenerative diseases." In the central nervous system (CNS), intercellular communication is essential for regulating brain activity and maintaining homeostasis.…”