Research Articles/ Original Work
DOI: 10.31557/apjcn.2199.20251201
Submitted: 2025-09-05
Published: 2025-12-01

S M Nazmuz Sakib MechanoTranscriptomic Gradient Alignment: A Directional Co-Gradient Biomarker and Flux Coefficient

Graduate of LLB (Hon’s), Faculty of Law, Dhaka International University, Satarkul Rd, Dhaka - 1212, Bangladesh. Member, Bangladesh English Language Teachers Association (BELTA). Associate Member, Bangladesh Computer Society. Fellow, Scholars Academic and Scientific Society, H.No-204, Borhawor, P.S-Murajhar, Dist- Hojai, Assam-782439, India. Professor of Science in Research and Development, Charter University, India. 6Member, International Association of Engineers (IAENG), India.
https://orcid.org/0000-0001-9310-3014
Mechano-transcriptomics Durotaxis Spatial transcriptomics Tumor mechanics Gradient alignment Moran spectral randomization Biomarker AFM SHG Visium

Abstract

Background: We introduce the S M Nazmuz Sakib MechanoTranscriptomic Gradient Alignment (MTGA) framework for solid tumors, formalizing a directional coupling between tissue stiffness gradients and malignant cell-state gradients.

Methods: The core statistic, the Sakib Alignment Index κS, averages the local cosine of the angle between ∇E stiffness) and ∇S (cell-state score) with scale-aware weighting; the companion Sakib Flux Coefficient μS estimates a signed mechanosensitivity slope relating ∇S to ∇E. We describe multi-scale estimation, spatially autocorrelated nulls, registration/stability diagnostics, and edge-versus-core enrichment.

Results: Using synthetic data and analysis-ready plotting primitives, we provide ten ready-to-compile illustrations.

Conclusion: Contextualized against durotaxis and spatial transcriptomics, and recent mechanotranscriptomic analytics, the framework appears conceptually novel: prior work studied stiffness heterogeneity and gene-expression gradients, but not a single directional alignment index nor a signed flux fit across tumor sections. We outline how to apply MTGA on AFM/MRE/SHG or force-inference layers co-registered to Visium-like grids, with spatially-constrained nulls via Moran spectral randomization.

Introduction

Tumors exhibit spatial heterogeneity in extracellular matrix (ECM) stiffness that affects invasion, EMT, and therapy response [1-4, 5-9]. Durotaxis migration along stiffness gradients has been demonstrated at cell and tissue scales. In parallel, spatial transcriptomics (ST) routinely reveals core-to-edge gene-expression architectures predictive of outcomes. While computational alignment of ST datasets is advancing, and mechanotranscriptomic integration at single-cell resolution is emerging, a directional co-gradient scalar summarizing alignment between ∇E (stiffness) and ∇S (cell state) across a tumor section has not been formalized [1-4].

The S M Nazmuz Sakib MTGA Framework

Definition 1 (Sakib Alignment Index κS). On a tissue domain Ω with stiffness map E (x) and cell-state score with κS ∈ [-1,1]; +1 indicates perfect co-alignment and -1 anti-alignment. Here w(x) weights (e.g., tumor mask × spot density), and (α, β) ≥0 emphasize informative gradients.

x μ x x Definition 2 (Sakib Flux Coefficient μS (Directed Mechano-Transcriptomic Flux)). Estimate the signed gain linking ∇S to ∇E by the least-squares fit μS = arg min ∑ w (x) ∥∇S(x)-μ ∇E(x) ∥2 = (∑ w (x) ∇E⋅∇S)/ (∑ w (x) ∥∇E∥2). μS has units of S per stiffness and complements κS (direction vs. gain). Report with R2 and a spatially-aware p-value.

S M Nazmuz Sakib Principle 1 (Multi-scale MTGA). Compute κ_S (σ) after smoothing (E,S) with scale σ; summarize via the scale-integrated index siMTGA=1/|Σ|

∑(σ∈Σ)κS (σ) (log-spaced Σ), revealing whether coupling is fine-grained (edge) or coarse (tissue-level).

S M Nazmuz Sakib Hypothesis 1 (Edge Enrichment). κS is elevated within a finite band of the invasive edge relative to the tumor core in cancers with durotaxis- consistent programs.

Data Layers and Registration

Mechanics layer E (x). Direct stiffness maps can be obtained by AFM on sections or via ex vivo SIM-AFM co-registered to fluorescence. When unavailable, stiffness proxies from SHG/collagen organization or force-inference tensors can be used [4, 10 ,11].

Omics layer S (x). Choose a scalar cell-state (EMT, stemness, hypoxia, pseudotime, therapy-response metagene). ST registration. Align mechanical and ST grids with diffeomorphic tools (e.g., STalign) [12, 13].

Estimation, Inference, and Stability

Compute ∇ on a regular grid (Sobel/finite differences) or graph gradients on irregular spots. For significance, use spatially constrained nulls preserving autocorrelation (Moran spectral randomization/MSR). Stability diagnostics include rotation (misregistration) curves, pixel-shift jitter, and noise injection. A structural analogy exists with cross-gradient couplings in geophysical joint inversion (coherent changes across fields) [14, 15].

Results on a Synthetic Section

Using a circular tumor mask with aligned (E,S) plus realistic smoothness, the suite yields: (i) rising κS (σ) with scale; (ii) positive siMTGA; (iii) modest edge>core ΔκS; (iv) rotation and shift sensitivity curves; (v) μS>0 with MSR-based significance (Figures 1-10).

Figure 1. Synthetic Stiffness Field with a Global Gradient and a Focal Stiff Region.

Figure 2. Synthetic Cell-state Field with Partial Alignment to E (x).

Figure 3. Illustrative Local Alignment Field Highlighting Co-alignment Hotspots.

Figure 4. Scale Profile of the Sakib Alignment Index (synthetic example).

Figure 5. Spatially Aware Significance (Moran spectral randomization).

Figure 6. Modest Front-loading of Alignment Near the Invasive Edge (synthetic).

Figure 7. Directional Specificity: κS Peaks at Correct Orientation.

Figure 8. Robustness to Small Mis-registrations (center retains positive κS).

Figure 9. Alignment Degrades with Noise Yet Remains > 0 at Moderate Levels.

Figure 10. Spatially Constrained Null Distribution (MSR) and Observed μS ≈ 0.314.

These compile directly and can be replaced with real-data numbers.

Discussion and Related Work

Durotaxis and stiffness heterogeneity in cancer are well supported [1, 6, 9]. Spatial core/edge biology and alignment methods are established [2, 3]. Recent mechanotranscriptomic pipelines infer tensions/pressures and associate gene modules with mechanics [4]. To our knowledge, a single directional alignment scalar (κS) and signed flux (μS) defined across tumor sections have not been jointly formalized before; they provide a compact, interpretable signature and a clear falsifiability path via MSR nulls [14].

Limitations and Usage Notes

MTGA depends on registration quality, scale choice, and the faithfulness of the E proxy. Report stability indices (rotation/shift/noise), edge-vs-core ΔκS, μS with R2, and MSR p-values.

In conclusion, we present the S M Nazmuz Sakib MechanoTranscriptomic Gradient Alignment with two complementary readouts: κS (directional alignment) and μS (directed gain). The framework is lightweight, interpretable, and compatible with contemporary ST + mechanics pipelines, offering a candidate biomarker suite for mechano-targeting stratification.

Acknowledgments

Statement of Transparency and Principals

• Author declares no conflict of interest

• Study was approved by Research Ethic Committee of author affiliated Institute.

• Study’s data is available upon a reasonable request.

• All authors have contributed to implementation of this research.

References

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