ReviewBiosensors2022
Microfluidic Compartmentalization Platforms for Single Cell Analysis.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Rapid Generation of High-Affinity Rabbit Anti-Mouse IgG Monoclonal Antibodies by High-Throughput Single-B-Cell Sorting and Recombinant Expression.Current issues in molecular biology · 2026Article
- Engineering next-generation microfluidic technologies for single-cell phenomics.Nature genetics · 2025Review
- Mini review: Persister cell control strategies.Frontiers in pharmacology · 2025Review
- A fluid-walled microfluidic platform for human neuron microcircuits and directed axotomy.Lab on a chip · 2024Article
- A mini review on recent progress of microfluidic systems for antibody development.Journal of diabetes and metabolic disorders · 2024Review
- Antibodies, repertoires and microdevices in antibody discovery and characterization.Lab on a chip · 2024Review
- Research progress in isolation and identification of rumen probiotics.Frontiers in cellular and infection microbiology · 2024Review
- Microfluidic design in single-cell sequencing and application to cancer precision medicine.Cell reports methods · 2023Review
- Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.Sensors (Basel, Switzerland) · 2023Review
- A Microfluidic Approach for Probing Heterogeneity in Cytotoxic T-Cells by Cell Pairing in Hydrogel Droplets.Micromachines · 2022Article
- Inference of B cell clonal families using heavy/light chain pairing information.PLoS computational biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.