Simultaneous pH Measurement in Endocytic and Cytosolic Compartments in Living Cells using Confocal Microscopy
Bibliographic record
Abstract
Intracellular pH is tightly regulated and differences in pH between the cytoplasm and organelles have been reported(1). Regulation of cellular pH is crucial for homeostatic control of physiological processes that include: protein, DNA and RNA synthesis, vesicular trafficking, cell growth and cell division. Alterations in cellular pH homeostasis can lead to detrimental functional changes and promote progression of various diseases(2). Various methods are available for measuring intracellular pH but very few of these allow simultaneous measurement of pH in the cytoplasm and in organelles. Here, we describe in detail a rapid and accurate method for the simultaneous measurement of cytoplasmic and organellar pH by using confocal microscopy on living cells(3). This goal is achieved with the use of two pH-sensing ratiometric dyes that possess selective cellular compartment partitioning. For instance, SNARF-1 is compartmentalized inside the cytoplasm whereas HPTS is compartmentalized inside endosomal/lysosomal organelles. Although HPTS is commonly used as a cytoplasmic pH indicator, this dye can specifically label vesicles along the endosomal-lysosomal pathway after being taken up by pinocytosis(3,4). Using these pH-sensing probes, it is possible to simultaneously measure pH within the endocytic and cytoplasmic compartments. The optimal excitation wavelength of HPTS varies depending on the pH while for SNARF-1, it is the optimal emission wavelength that varies. Following loading with SNARF-1 and HPTS, cells are cultured in different pH-calibrated solutions to construct a pH standard curve for each probe. Cell imaging by confocal microscopy allows elimination of artifacts and background noise. Because of the spectral properties of HPTS, this probe is better suited for measurement of the mildly acidic endosomal compartment or to demonstrate alkalinization of the endosomal/lysosomal organelles. This method simplifies data analysis, improves accuracy of pH measurements and can be used to address fundamental questions related to pH modulation during cell responses to external challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".