Biology subjects

Jin, D.

Publications and source records attributed to Jin, D..

2 recordsLinked to original sources

bioRxiv2018

Enrichment of short mutant cell-free DNA fragments enhanced detection of pancreatic cancer

Analysis of cell-free DNA (cfDNA) is promising for broad applications in clinical settings, but with significant bias towards late-stage cancers. Although recent studies have discussed the diverse and degraded nature of cfDNA molecules, little is known about its impact on the practice of cfDNA analysis. Here we reported a new targeted sequencing by combining single-strand library preparation and target capture (SLHC-seq). By applying the new technology in plasma cfDNA from pancreatic cancer patients, we achieved higher efficiency in analysis of mutations than previously reported using other detection assays. SLHC-seq rescued short or damaged cfDNA fragments along to increase the sensitivity and accuracy of circulating-tumor DNA detection. Most importantly, we found that the small mutant fragments are prevalent in early-stage patients, which provides strong evidence for fragment size-based early detection of pancreatic cancer. Collectively, the new pipeline enhanced our understanding of cfDNA biology and provide new insights for liquid biopsy.

cancer biology↗

bioRxiv2018

Generative adversarial network (GAN) enabled on-chip contact microscopy

We demonstrate a deep learning based contact imaging on a CMOS chip to achieve [~]1 m spatial resolution over a large field of view of [~]24 mm2. By using regular LED illumination, we acquire the single lower-resolution image of the objects placed approximate to the sensor with unit fringe magnification. For the raw contact-mode lens-free image, the pixel size of the sensor chip limits the spatial resolution. We apply a generative and adversarial network (GAN), a type of deep learning algorithm, to circumvent this limitation and effectively recover much higher resolution image of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. This GAN-contact imaging approach eliminates the need of either lens or multi-frame acquisition, being very handy and cost-effective. We demonstrate the success of this approach by imaging the proliferation dynamics of cells directly cultured on the chip.

biophysics↗