A comparative study of CARE 2D and N2V 2D for tissue-specific denoising in second harmonic generation imaging.

This study explored the application of deep learning in second harmonic generation (SHG) microscopy, a rapidly growing area. This study focuses on the impact of glycerol concentration on image noise in SHG microscopy and compares two image restoration techniques: Noise-to-Void 2D (N2V 2D, no reference image restoration) and content-aware image restoration (CARE 2D, full reference image restoration). We demonstrated that N2V 2D effectively restored the images affected by high glycerol concentrations. To reduce sample exposure and damage, this study further addresses low-power SHG imaging by reducing the laser power by 70% using deep learning techniques. CARE 2D excels in preserving detailed structures, whereas N2V 2D maintains natural muscle structure. This study highlights the strengths and limitations of these models in specific SHG microscopy applications, offering valuable insights and potential advancements in the field .

Second harmonic generation microscopy: a powerful tool for bio-imaging.

Second harmonic generation (SHG) microscopy is an important optical imaging technique in a variety of applications. This article describes the history and physical principles of SHG microscopy and its more advanced variants, as well as their strengths and weaknesses in biomedical applications. It also provides an overview of SHG and advanced SHG imaging in neuroscience and microtubule imaging and how these methods can aid in understanding microtubule formation, structuration, and involvement in neuronal function. Finally, we offer a perspective on the future of these methods and how technological advancements can help make SHG microscopy a more widely adopted imaging technique.

Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies.

Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.

Phosphorylation of eIF4E in the stroma drives the production and spatial organisation of collagen type I in the mammary gland.

The extracellular matrix (ECM) plays critical roles in breast cancer development. Whether ECM composition is regulated by the phosphorylation of eIF4E on serine 209, an event required for tumorigenesis, has not been explored. Herein, we used proteomics and mouse modeling to investigate the impact of mutating serine 209 to alanine on eIF4E (i.e., S209A) on mammary gland (MG) ECM. The proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028953. We discovered that S209A knock-in mice, expressing a non-phosphorylatable form of eIF4E, have less collagen-I deposition in native and tumor-bearing MGs, leading to altered tumor cell invasion. Additionally, phospho-eIF4E deficiency impacts collagen topology; fibers at the tumor-stroma boundary in phospho-eIF4E-deficient mice run parallel to the tumor edge but radiate outwards in wild-type mice. Finally, a phospho-eIF4E-deficient tumor microenvironment resists anti-PD-1 therapy-induced collagen deposition, correlating with an increased anti-tumor response to immunotherapy. Clinically, we showed that collagen-I and phospho-eIF4E are positively correlated in human breast cancer samples, and that stromal phospho-eIF4E expression is influenced by tumor proximity. Together, our work defines the importance of phosphorylation of eIF4E on S209 as a regulator of MG collagen architecture in the tumor microenvironment, thereby positioning phospho-eIF4E as a therapeutic target to augment response to therapy.