ReviewThe Analyst2019
Quantitative imaging of lipid droplets in single cells.
Review in The Analyst, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Single-cell Raman imaging reveals fructose impairs brown adipocyte differentiation.Biosensors & bioelectronics · 2025Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- Differential lipid analysis of oxaliplatin-sensitive and resistant HCT116 cells reveals different levels of drug-induced lipid droplet formation.Analytical and bioanalytical chemistry · 2024Article
- Emergence of Lipid Droplets in the Mechanisms of Carcinogenesis and Therapeutic Responses.Cancers · 2023Review
- Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images.Frontiers in chemistry · 2023Article
- Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls.Annual review of analytical chemistry (Palo Alto, Calif.) · 2022Review
- Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages.Genome research · 2022Article
- Assessing fatty acid-induced lipotoxicity and its therapeutic potential in glioblastoma using stimulated Raman microscopy.Scientific reports · 2021Article
- μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells.Lab on a chip · 2020Article
- An American Physiological Society cross-journal Call for Papers on "Deconstructing Organs: Single-Cell Analyses, Decellularized Organs, Organoids, and Organ-on-a-Chip Models".American journal of physiology. Lung cellular and molecular physiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The combination of next generation sequencing (NGS) and automated liquid handling platforms has led to a revolution in single-cell genomic studies. However, many molecules that are critical to understanding the functional roles of cells in a complex tissue or organs, are not directly encoded in the genome, and therefore cannot be profiled with NGS. Lipids, for example, play a critical role in many metabolic processes but cannot be detected by sequencing. Recent developments in quantitative imaging, particularly coherent Raman scattering (CRS) techniques, have produced a suite of tools for studying lipid content in single cells. This article reviews CRS imaging and computational image processing techniques for non-destructive profiling of dynamic changes in lipid composition and spatial distribution at the single-cell level. As quantitative CRS imaging progresses synergistically with microfluidic and microscopic platforms for single-cell genomic analysis, we anticipate that these techniques will bring researchers closer towards combined lipidomic and genomic 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.