Measuring Absolute Cell Volume Using Quantitative-Phase Digital Holographic Microscopy and a Low-Cost, Open-Source, and 3D-Printed Flow Chamber
Bibliographic record
Abstract
Although cell volume is a critical cell parameter and acute changes of its regulation mechanisms can reveal pathophysiological states, its accurate measurement remains difficult. On the other hand, custom-made devices are now possible through the application of 3D printing that meet the needs of certain specific applications. Therefore, in this article we describe the development of a low-cost, open-source, and 3D-printed flow chamber optimized to perform non-invasive accurate measurements of important cell biophysical parameters. This measurement development includes the absolute cell volume, mean cell thickness, and whole-cell refractive index using quantitative-phase digital holographic microscopy. Specifically, this advancement makes it possible to develop a flow chamber that preserves cell viability while exhibiting a fast washout process for the two-liquid decoupling procedure - an experimental procedure allowing to separately measure the intracellular refractive index and cell thickness from the quantitative-phase signal - characterized by a homogeneous return of the extracellular refractive index over the entire imaging slit toward the known value of the last perfusion solution. Such a fast washout process has been extensively characterized by monitoring the time required to obtain a stabilization of the quantitative-phase signal following the perfusion of the two liquids. A more rapid quantitative-phase signal stabilization time results in fewer cell changes, including important cell position and shape modifications. These changes likely take place over the washout process time and ultimately result in important artefacts to all cell biophysical measurements. The proposed flow chamber has been used to perform measurements of the whole-cell refractive index, mean cell thickness, and absolute cell volume of three cell types in culture during resting state, and hypo-osmotic and hyper-osmotic challenges. For a typical cell body, these biophysical parameters are measured with a precision of 0.0006, 100 nm, and 50 µm3 respectively. Finally, the design files of the flow chamber will be shared with the scientific community through an open-source model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".