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arxiv: 2410.00532 · v5 · submitted 2024-10-01 · 🧬 q-bio.QM

smICA: Open-Source Software for Quantitative, Lifetime-Resolved Mapping of Absolute Fluorophore Concentrations in Living Cells

Tomasz Kalwarczyk , Grzegorz Bubak , Jaros{\l}aw Michalski , Antoni Lis , Karina Kwapiszewska , Marta Pilz , Adam Mamot , Olga Perzanowska
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Joanna Kowalska Jacek Jemielity Robert Ho{\l}yst
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Pith reviewed 2026-05-23 20:18 UTC · model grok-4.3

classification 🧬 q-bio.QM
keywords smICAabsolute fluorophore concentrationlifetime-resolved imagingfluorescence microscopysingle-cell analysisFCS validationopen-source softwaremRNA concentration tracking
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The pith

smICA software converts fluorescence images into maps of absolute fluorophore concentrations inside living cells.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents smICA as an open-source tool that turns intensity and lifetime data from fluorescence microscopy into absolute concentration values for fluorophores in living cells. It adds lifetime filtering to clean the signal, segments cells by intensity, and operates on images with only a few photons per pixel. The method shortens the time to obtain mean concentrations per cell relative to repeated fluorescence correlation spectroscopy runs. Validation experiments compared smICA results to FCS measurements on polymers in solution and on polymers plus EGFP inside cells, with a further demonstration tracking fluorescent mRNA levels over time.

Core claim

smICA offers quantitative mapping of absolute fluorophore concentrations, lifetime-resolved filtering methods of the signal, intensity-based cell segmentation, and requires only a few photons per pixel. Validation against standard fluorescence correlation spectroscopy measurements by performing in vitro (polymers in aqueous solution) and in vivo (polymers and EGFP in living cells) experiments shows agreement, and the approach reduces the time required to determine the mean concentration per cell compared to the standard FCS measurement performed in multiple posts. Exemplary studies on the time evolution of fluorescently labelled mRNA concentration in living cells are presented.

What carries the argument

The smICA (Single-Molecule Image to Concentration Analyser) processing pipeline, which converts raw fluorescence intensity and lifetime images into absolute concentration maps after lifetime filtering and intensity-based segmentation.

If this is right

  • Concentration maps become available across whole cells instead of at single points.
  • Time series of biomolecule concentrations such as mRNA can be followed in individual living cells.
  • Average concentration per cell is obtained faster than with multiple separate FCS measurements.
  • Studies of protein expression, degradation, and enzymatic reactions become feasible at the single-cell level with low photon counts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pipeline could be tested on additional fluorophores or multi-color samples to check generality beyond the reported cases.
  • Integration with other live-cell imaging modalities might allow simultaneous measurement of concentration and localization dynamics.
  • High-throughput application to many cells in parallel could support screening of cellular responses to perturbations.

Load-bearing premise

That fluorescence intensity and lifetime data can be converted to absolute concentrations using the described processing steps without unaccounted calibration factors or cell-specific effects that differ from the tested polymers and EGFP cases.

What would settle it

A side-by-side measurement in the same living cells where smICA-derived concentrations differ substantially from independent absolute quantification by another method such as quantitative Western blot or calibrated mass spectrometry.

Figures

Figures reproduced from arXiv: 2410.00532 by Adam Mamot, Antoni Lis, Grzegorz Bubak, Jacek Jemielity, Jaros{\l}aw Michalski, Joanna Kowalska, Karina Kwapiszewska, Marta Pilz, Olga Perzanowska, Robert Ho{\l}yst, Tomasz Kalwarczyk.

Figure 1
Figure 1. Figure 1: The stripped region marked in the figure relates to the places where the FCS measurements were typically performed. The dashed curve represents the cell boundary. The FCS measurements were chosen to omit the endoplasmic reticulum and nucleus. sum of detected photons for each second of measurement and fitted the result with homogeneous linear function (y = ax). Cntrate is equal to the slope of the fitted fu… view at source ↗
Figure 2
Figure 2. Figure 2: The figure shows the workflow scheme used in the concentration imaging method. First, we calibrate the FCS setup using a dye with a known diffusion coeffi￾cient. From calibration measurements, we get omega - the width of the focal volume. Next, we performed the FCS measurements on the target samples to find the molecu￾lar brightness, B. Finally, the raster imaging was performed using the laser scanning con… view at source ↗
Figure 3
Figure 3. Figure 3: Figure a depicts a plot of the fluorescence decay patterns for the raw signal and the filtered signal acquired for the TRITC-labeled dextran polymer. We applied the filtering based on the afterpulsing and background removal according to the algorithm described in references 5, and 9. Panel b shows violin plots representing the distributions of the Cim/CFCS ratio obtained for different region-of-interest si… view at source ↗
Figure 4
Figure 4. Figure 4: Figure a depicts raw fluorescence decay patterns acquired in the pulse inter￾leaved excitation, PIE, mode in living cells where two fluorophores (EGFP and TRIC￾labeled dextran) were present. Although the photons were registered in the TRITC channel, some GFP molecules were excited due to the long tail of the excitation spectra of the fluorescent protein. By analogy, Figure b depicts fluorescence dacy for t… view at source ↗
Figure 5
Figure 5. Figure 5: The top panel of the figure shows histograms of the concentration calculated for each image pixel. The calculations were restricted to the ROI marked with yellow lines on the images below the histograms. The histograms and images represent the data for a single cell tracked in time (cell 3) at t = 104, 230, and 609 minutes after the mRNA injection into the cells. The red line represents the mean concentrat… view at source ↗
Figure 6
Figure 6. Figure 6: Figure shows the time changes in the total number of photons registered in the region of interest for a single cell (cell 3). We compared the total number of photons registered during the first and the last frame of each time data point. During the acquisition of the frames, a small drop in the total number of photons was observed, probably due to the photobleaching of the dyes, even though we reduced the … view at source ↗
read the original abstract

Advanced microscopy techniques are essential in biomedical research for visualising and tracking biomolecules within living cells and their compartments. Conventional fluorescence microscopy methods, however, often struggle with accurately measuring the absolute concentrations of fluorescent probes in living cells. To overcome these limitations, we introduce an open-source analysis tool, smICA (Single-Molecule Image to Concentration Analyser). The smICA method offers quantitative mapping of absolute fluorophore concentrations, lifetime-resolved filtering methods of the signal, intensity-based cell segmentation, and requires only a few photons per pixel. Our approach also reduces the time required to determine the mean concentration per cell compared to the standard FCS measurement performed in multiple posts. To highlight the robustness of the method, we validated it against standard fluorescence correlation spectroscopy (FCS) measurements by performing in vitro (polymers in aqueous solution) and in vivo (polymers and EGFP in living cells) experiments. Finally, we present exemplary studies on the time evolution of fluorescently labelled mRNA concentration in living cells. The presented methodology, along with the software, is a promising tool for quantitative single-cell studies, including, but not limited to, protein expression, biomolecule degradation (such as proteins and mRNA), and monitoring enzymatic reactions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces smICA, an open-source tool for mapping absolute fluorophore concentrations in living cells from fluorescence intensity and lifetime data. It incorporates lifetime-resolved signal filtering, intensity-based cell segmentation, and claims operation with few photons per pixel. The central claim is that the method yields absolute concentrations (not merely relative) and reduces measurement time relative to multi-point FCS; this is supported by in-vitro validation on polymers in solution and in-vivo validation on polymers plus EGFP in cells, plus an example application to time-dependent mRNA concentrations.

Significance. If the absolute-scale mapping is free of unaccounted cell- or label-specific factors, the approach would enable faster, photon-efficient absolute quantification in single cells, directly benefiting studies of protein expression, mRNA dynamics, and enzymatic activity. The open-source release and explicit few-photon capability are concrete strengths that lower barriers to adoption.

major comments (2)
  1. [Abstract / Validation section] Abstract and validation description: the claim of absolute (rather than relative) concentrations is load-bearing, yet the text supplies no explicit calibration equation, no propagation of uncertainty from lifetime/intensity observables to concentration, and no bounding of cell-specific multipliers (quantum yield, local refractive index, binding, or autofluorescence leakage) that could differ from the polymer/EGFP test cases. Without these, the FCS cross-validation cannot establish that the reported absolute values are scale-correct beyond the specific labels and cell types examined.
  2. [Validation experiments] Methods / Results on in-vivo experiments: the manuscript reports FCS agreement for polymers and EGFP but does not describe whether the same calibration constants were applied across cell types or whether independent measurements (e.g., refractive-index variation or quantum-yield checks) were performed to test the assumption that the intensity-to-concentration conversion remains parameter-free outside the calibration set.
minor comments (2)
  1. [Abstract] The abstract states that the method 'requires only a few photons per pixel' but does not quantify the minimum photon count or SNR threshold used in the segmentation and filtering steps.
  2. [Software section] Software availability is mentioned but the manuscript does not include a direct link, version number, or example input/output files that would allow immediate reproduction of the reported concentration maps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of absolute concentration mapping. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / Validation section] Abstract and validation description: the claim of absolute (rather than relative) concentrations is load-bearing, yet the text supplies no explicit calibration equation, no propagation of uncertainty from lifetime/intensity observables to concentration, and no bounding of cell-specific multipliers (quantum yield, local refractive index, binding, or autofluorescence leakage) that could differ from the polymer/EGFP test cases. Without these, the FCS cross-validation cannot establish that the reported absolute values are scale-correct beyond the specific labels and cell types examined.

    Authors: We agree that explicit presentation of the calibration equation and uncertainty propagation will strengthen the manuscript. The absolute scale is obtained from in-vitro polymer solutions of known concentration; intensity is converted via a factor incorporating measured lifetime, detection efficiency, and a single scaling constant fitted to the polymer data. In the revision we will add the full equation, its derivation, and analytic uncertainty propagation to the Methods section. We will also expand the Discussion to state the assumptions (constant quantum yield and refractive index within the tested range) and note that FCS agreement validates the scale only for the labels and cell types examined, without claiming universality. revision: yes

  2. Referee: [Validation experiments] Methods / Results on in-vivo experiments: the manuscript reports FCS agreement for polymers and EGFP but does not describe whether the same calibration constants were applied across cell types or whether independent measurements (e.g., refractive-index variation or quantum-yield checks) were performed to test the assumption that the intensity-to-concentration conversion remains parameter-free outside the calibration set.

    Authors: The identical calibration constants derived from the in-vitro polymer data were applied uniformly to all in-vivo experiments (polymers and EGFP in multiple cell types). We did not conduct separate refractive-index or quantum-yield measurements per cell type; the FCS cross-validation was used as the primary check. In the revision we will explicitly state in Methods that the calibration constants were held fixed and add a short paragraph in Results/Discussion acknowledging the absence of those independent checks while noting that the observed FCS agreement supports the assumption within the tested conditions. revision: partial

Circularity Check

0 steps flagged

No circularity; method is empirically validated against independent FCS data

full rationale

The paper presents smICA as an analysis tool that processes intensity and lifetime data into concentration maps, with explicit validation against separate FCS measurements in polymers (in vitro) and EGFP/polymers (in vivo). No equations or steps are shown that define concentration via a fitted parameter that is then re-used as a prediction, nor any self-citation chain that bears the central claim. The mapping is presented as a processing pipeline whose accuracy is tested externally rather than derived tautologically from its own inputs. This is the expected non-circular outcome for a methods/software paper grounded in experimental cross-checks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.0 · 5801 in / 1000 out tokens · 26951 ms · 2026-05-23T20:18:43.942305+00:00 · methodology

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 1 internal anchor

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