The team turned to one of the most intensively studied systems in developmental biology: the fruit fly embryo. In the early Drosophila embryo, maternal morphogens lay down the initial coordinates along the anterior-posterior axis, the head-to-tail line of the body. These inputs drive a network of four gap genes, knirps, Krüppel, hunchback, and giant, whose broad expression domains in turn pattern the striped expression of pair-rule genes such as even-skipped, paired, and runt. Those stripes ultimately map out the segmented body plan of the adult fly. Because gene expression in these embryos is extraordinarily reproducible between individuals, with the variance of stripe positions measuring roughly half the distance between adjacent nuclei, the system offers an ideal testing ground for quantitative, information-theoretic analyses of how cells decode their position.

Earlier work had established that gap gene expression carries enough information for a cell to estimate its position along the anterior-posterior axis with an accuracy of about one percent of the embryo’s length. That sounds extraordinarily precise, and it is, but a recent theoretical analysis revealed a paradox. One percent precision is not sufficient to uniquely determine a cell’s position among the roughly ninety nuclei that line the axis. In principle, two adjacent cells could decode their positions in the wrong order, swapping identities. Researchers proposed that long-range correlations in gene expression along the entire axis could resolve this so-called information gap, because such correlations constrain the space of possible collective expression patterns and thereby reduce how much information each individual cell needs. But that raised an obvious biological objection: no single cell can plausibly access information about expression patterns far away on the other side of the embryo.

The new study resolves this tension with an elegant insight. The authors showed mathematically that the long-range correlations invoked in previous work can emerge entirely from correlations between immediately adjacent cells. When neighboring positions along the axis have highly correlated expression levels, these local relationships propagate to generate the embryo-wide correlation structure, and above a critical threshold they close the information gap. Crucially, the required threshold corresponds to pairwise correlations between adjacent nuclei exceeding 0.95, a distance across which cells can plausibly interact, whether through direct contact, diffusing molecules, or shared mechanical cues. When the researchers examined experimental measurements of gap gene and pair-rule gene expression in wild-type fly embryos, they found that pairwise correlations between adjacent positions did indeed exceed 0.95 across the full length of the axis.

Building on this observation, the team formulated a neighborhood-informed decoder, a probabilistic framework that estimates a cell’s position using not only the cell’s own gene expression but also the expression levels of its immediate neighbors. Formally, the framework compares a cell-independent decoder, which has access only to the expression profile g of a single position, with a neighborhood-informed decoder, which also receives the expression profiles of flanking positions. Under standard assumptions that gene expression fluctuations follow a Gaussian distribution, the authors derived a closed-form expression for the positional error in each case and proved a key result: adding neighborhood information can never increase the positional error, and in practice it strictly decreases it. The amount of positional information, measured in bits, is inversely related to that error, so shrinking the error means gaining information.

The quantitative payoff was dramatic. Applying the neighborhood-informed decoder to fluorescence measurements from wild-type embryos, the researchers found that the standard deviation of predicted position dropped to roughly 0.2 percent of embryo length, nearly an order of magnitude smaller than the roughly one percent achieved by cell-independent decoding. In information terms, the neighborhood-informed decoder extracted an average of 7.0 bits of positional information per cell, compared with 4.4 bits for the cell-independent decoder. Since uniquely specifying a cell’s location along the axis requires about 6.49 bits, the neighborhood-informed decoder clears the bar while the cell-independent decoder falls short. In other words, the microenvironment contains exactly the information that individual cells appear to lack.

The framework also proved its worth as a predictive tool. Trained on hundreds of embryos, the neighborhood-informed decoder produced posterior distributions over positions that were more sharply peaked around the true locations, with lower prediction errors and lower ambiguity than the cell-independent alternative, and the improvements were statistically significant across virtually all embryo-to-embryo comparisons. The decoded positions could then be used to reconstruct downstream pair-rule gene expression patterns, and here too the neighborhood-informed approach won decisively, yielding significantly more accurate reconstructions of the even-skipped, paired, and runt stripe patterns across the axis. The advantage persisted even in mutant embryos whose maternal signals, such as Bicoid and Nanos, had been genetically perturbed, producing altered gap gene landscapes and shifted stripes. Even there, decoding with neighborhood information reduced the uncertainty of inferred positions and better predicted the mutant pair-rule patterns, suggesting that the mapping from gap gene expression to positional readout genuinely exploits local context rather than isolated cellular measurements.

To test whether the principle extends beyond the fly, the authors applied the same analysis to two mammalian systems. The first was the gastruloid, an in vitro model of early mammalian development in which mouse embryonic stem cells self-organize into elongated structures with anterior-posterior patterning. Analyzing the germ-layer markers Bra, Cdx2, FoxC1, and Sox2 along the gastruloid axis, the team found that cell-independent decoding again fell short of the roughly 5.2 bits needed for unique localization, delivering only about 4.6 bits on average, while the neighborhood-informed decoder reached 6.8 bits, comfortably above the requirement. The second system was the developing mouse neural tube, where the antiparallel signaling gradients of Sonic hedgehog and bone morphogenetic proteins pattern neural progenitors along the dorsal-ventral axis. Once more, neighborhood-informed decoding supplied enough information, about 6.1 bits against a requirement of 5.4 bits, to uniquely specify positions along 89 percent of the axis, whereas cell-independent decoding was insufficient along most of it. The consistency across fly embryos, synthetic mammalian tissues, and the neural tube suggests that neighborhood-based positional decoding may be a general feature of developmental patterning rather than a quirk of one model organism.

The authors are careful about what their framework does and does not claim. The analysis is deliberately agnostic about mechanism: information could flow between neighbors through direct cell-cell contacts, diffusing signals, altered membrane mechanics, or adhesive molecules, and the information-theoretic results hold regardless of which channel is used. The study also cannot determine whether the spatial correlations were already baked into the cells when the data were collected, or whether neighborhood interactions actively help cells refine their positions as development proceeds; perturbation experiments targeting cell-cell interactions would be needed to distinguish these scenarios. The framework additionally requires data aligned along a single well-defined axis with enough replicates to estimate covariance matrices, which is why the authors focused on these three well-characterized systems. Future extensions could move to two- and three-dimensional contexts and to spatial transcriptomics data, potentially using graph-based definitions of neighborhoods that go beyond simple physical adjacency.

Even with those caveats, the implications are broad. The work reframes a foundational question in developmental biology: instead of asking only what a cell knows on its own, it asks what a cell and its companions collectively know. The finding that local microenvironments carry the missing bits needed for unique identity specification offers a satisfying resolution to the positional information gap and provides a quantitative tool that can be applied wherever reproducible patterning emerges. It also hints at why tissues are so robust: a decoding strategy that pools information across neighbors is inherently resilient to the noise and variability that plague individual measurements. As researchers continue to map the conversations cells hold with their surroundings, this study suggests that the answers to how an embryo builds itself may be written not in any single cell, but in the shared language of the neighborhood.

Subject of Research: Neighborhood-informed positional information and cell identity specification during embryonic development

Article Title: Neighborhood-informed positional information for precise cell identity specification

Article References: Erez, M., Friedman, R., & Nitzan, M. (2026). Neighborhood-informed positional information for precise cell identity specification. Molecular Systems Biology, 22(7), 1118-1131. https://doi.org/10.1038/s44320-026-00211-y

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00211-y

Keywords: positional information, information theory, Drosophila embryo, gap genes, pair-rule genes, morphogen gradients, cell fate, gastruloids, neural tube, developmental biology, gene expression decoding, microenvironment