A novel method for determining murine skeletal muscle fiber type using autofluorescence lifetimes.
Carlo MannoEshwar R TammineniLourdes C FigueroaYuriana Oropeza-AlmazánEduardo RíosPublished in: The Journal of general physiology (2022)
This work describes a simple way to identify fiber types in living muscles by fluorescence lifetime imaging microscopy (FLIM). We quantified the mean values of lifetimes τ1 and τ2 derived from a two-exponential fit in freshly dissected mouse flexor digitorum brevis (FDB) and soleus muscles. While τ1 values changed following a bimodal behavior between muscles, the distribution of τ2 is shifted to higher values in FDB. To understand the origin of this difference, we obtained maps of autofluorescence lifetimes of flavin mononucleotide and dinucleotide (FMN/FAD) in cryosections, where excitation was set at 440 nm and emission at a bandwidth of between 500 and 570 nm, and paired them with immunofluorescence images of myosin heavy chain isoforms, which allowed identification of fiber types. In soleus, τ2 was 3.16 ns for type I (SD 0.11, 97 fibers), 3.45 ns for IIA (0.10, 69), and 3.46 ns for IIX (0.12, 65). In FDB muscle, τ2 was 3.17 ns for type I (0.08, 22), 3.46 ns for IIA (0.16, 48), and 3.66 ns for IIX (0.15, 43). From τ2 distributions, it follows that an FDB fiber with τ2 > 3.3 ns is expected to be of type II, and of type I otherwise. This simple classification method has first and second kind errors estimated at 0.02 and 0.10, which can be lowered by reducing the threshold for identification of type I and increasing it for type II. Lifetime maps of autofluorescence, therefore, constitute a tool to identify fiber types that, for being practical, fast, and noninvasive, can be applied in living tissue without compromising other experimental interventions.
Keyphrases
- dengue virus
- skeletal muscle
- zika virus
- deep learning
- photodynamic therapy
- machine learning
- single molecule
- type diabetes
- optical coherence tomography
- physical activity
- high throughput
- aedes aegypti
- metabolic syndrome
- adipose tissue
- patient safety
- binding protein
- adverse drug
- fluorescence imaging
- bioinformatics analysis
- single cell
- solid state