Single-cell RNA-seq reveals the cellular heterogeneity inherent in the population of cells, which is very important in many clinical and research applications. Recent advances in droplet microfluidics have achieved the automatic isolation, lysis, and labeling of single cells in droplet compartments without complex instrumentation. However, barcoding errors occurring in the cell encapsulation process because of the multiple-beads-in-droplet and insufficient throughput because of the low concentration of beads for avoiding multiple-beads-in-a-droplet remain important challenges for precise and efficient expression profiling of single cells. In this study, we developed a new droplet-based microfluidic platform that significantly improved the throughput while reducing barcoding errors through deterministic encapsulation of inertially ordered beads. Highly concentrated beads containing oligonucleotide barcodes were spontaneously ordered in a spiral channel by an inertial effect, which were in turn encapsulated in droplets one-by-one, while cells were simultaneously encapsulated in the droplets. The deterministic encapsulation of beads resulted in a high fraction of single-bead-in-a-droplet and rare multiple-beads-in-a-droplet although the bead concentration increased to 1000 μl, which diminished barcoding errors and enabled accurate high-throughput barcoding. We successfully validated our device with single-cell RNA-seq. In addition, we found that multiple-beads-in-a-droplet, generated using a normal Drop-Seq device with a high concentration of beads, underestimated transcript numbers and overestimated cell numbers. This accurate high-throughput platform can expand the capability and practicality of Drop-Seq in single-cell analysis.

Download full-text PDF

Source
http://dx.doi.org/10.1039/c7lc01284eDOI Listing

Publication Analysis

Top Keywords

barcoding errors
12
droplet microfluidics
8
single-cell rna-seq
8
single cells
8
concentration beads
8
deterministic encapsulation
8
encapsulated droplets
8
accurate high-throughput
8
beads
5
inertial-ordering-assisted droplet
4

Similar Publications

In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.

View Article and Find Full Text PDF

Optimizing dual energy X-ray image enhancement using a novel hybrid fusion method.

J Xray Sci Technol

December 2024

School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin, China.

Background: Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.

View Article and Find Full Text PDF

Background: Phenomenological psychopathologists have recently highlighted how people with delusions experience multiple realities (delusional and non-delusional) and have suggested this double bookkeeping cannot be explained via predictive processing. Here, we present data from Kamin blocking and extinction learning that show how predictive processing might, in principle, explain a pervasive sense of dual reality.

Methods: This cross-sectional study involved three participant groups: patients with schizophrenia (SZ; n=42), healthy participants with elevated esoteric beliefs (EEB; clairaudient psychics; n=31), and heathy controls (with neither illness nor significant delusional ideation, n=62).

View Article and Find Full Text PDF

Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC-MS urinary metabolomics for diseases screening.

Int J Med Inform

December 2024

Department of Genetics and Metabolism, the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China. Electronic address:

Background: Gas chromatography-mass spectrometry (GC-MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC-MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC-MS organic acid profiles.

Methods: Based on 355,197 GC-MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated.

View Article and Find Full Text PDF

Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorithms. Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. However, such labels are typically highly time-consuming, error-prone and expensive to produce.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!