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Welcome to The Visible Embryo, a comprehensive educational resource on human development from conception to birth.

The Visible Embryo provides visual references for changes in fetal development throughout pregnancy and can be navigated via fetal development or maternal changes.

The National Institutes of Child Health and Human Development awarded Phase I and Phase II Small Business Innovative Research Grants to develop The Visible Embryo. Initally designed to evaluate the internet as a teaching tool for first year medical students, The Visible Embryo is linked to over 600 educational institutions and is viewed by more than one million visitors each month.

Today, The Visible Embryo is linked to over 600 educational institutions and is viewed by more than 1 million visitors each month. The field of early embryology has grown to include the identification of the stem cell as not only critical to organogenesis in the embryo, but equally critical to organ function and repair in the adult human. The identification and understanding of genetic malfunction, inflammatory responses, and the progression in chronic disease, begins with a grounding in primary cellular and systemic functions manifested in the study of the early embryo.

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Pregnancy Timeline by SemestersFetal liver is producing blood cellsHead may position into pelvisBrain convolutions beginFull TermWhite fat begins to be madeWhite fat begins to be madeHead may position into pelvisImmune system beginningImmune system beginningPeriod of rapid brain growthBrain convolutions beginLungs begin to produce surfactantSensory brain waves begin to activateSensory brain waves begin to activateInner Ear Bones HardenBone marrow starts making blood cellsBone marrow starts making blood cellsBrown fat surrounds lymphatic systemFetal sexual organs visibleFinger and toe prints appearFinger and toe prints appearHeartbeat can be detectedHeartbeat can be detectedBasic Brain Structure in PlaceThe Appearance of SomitesFirst Detectable Brain WavesA Four Chambered HeartBeginning Cerebral HemispheresFemale Reproductive SystemEnd of Embryonic PeriodEnd of Embryonic PeriodFirst Thin Layer of Skin AppearsThird TrimesterSecond TrimesterFirst TrimesterFertilizationDevelopmental Timeline
CLICK ON weeks 0 - 40 and follow along every 2 weeks of fetal development
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Home | Pregnancy Timeline | News Alerts |News Archive Feb 11, 2014

 

Ribonucleic acid (RNA) - here depicted in red - is a family of large molecules
that perform multiple roles in decoding, regulation, and expression of DNA genes.






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Tracing the uniqueness of cells using math

Stem cells can turn into heart cells, skin cells can mutate into cancer cells; even cells of the same tissue type exhibit small differences. Currently, scientists use labor intense analysis to investigate these small differences. But such methods have considerable inaccuracies too. Now, scientists have found a way to simplify and improve their analysis — using math.

Each cell in our body is unique. Even cells of the same tissue type that look identical under the microscope are slightly different from each other.

To understand how a heart cell can develop from a stem cell, why one beta-cell produces insulin and another does not, or why a normal tissue cell suddenly mutates into a cancer cell, scientists at the Technische Universitaet Muenchen (TUM), the Helmholtz Zentrum Muenchen and the University of Virginia (USA) studied ribonucleic acid — RNA.


Proteins are constantly being assembled and disassembled in cells.

RNA molecules read the DNA blueprints for proteins and then initiate the production of each protein found.

Scientists around the world, in the last few years, have developed sequencing methods for detecting all active RNA molecules within a single cell at specific times.


At the end of December 2013 the journal Nature Methods declared single-cell sequencing the "Method of the Year." However, analysis of individual cells is still extremely complex, as any handling of cells generates errors and inaccuracies. Small differences in gene regulation can be overwhelmed by such cellular "noise."

Easier and more accurate, thanks to statistics

Scientists led by Professor Fabian Theis, Chair of Mathematical Modeling of Biological Systems at the Technische Universitaet Muenchen, and director of the Institute of Computational Biology at the Helmholtz Zentrum Muenchen, have now found a way to considerably improve single-cell analysis using methods of statistical averaging.

Instead of analysing just one cell, they look at 16 to 80 samples containing ten cells each. "A sample of ten cells is much easier to handle," says Professor Theis. "With ten times the amount of cell material, the influences of ambient conditions can be largely overcome."

However, cells with different properties are then randomly distributed in each sample of ten cells. So, Theis's collaborator Christiane Fuchs developed statistical methods to identify single-cell properties within a mixture of signals.

Combining model and experiment

Using known biologic facts, Theis and Fuchs modeled the distribution of genes exhibiting two well-defined regulatory states. Working with biologists Kevin Janes and Sameer Bajikar, University of Virginia, Charlottesville (USA), they were able to create highly accurate predictions of single cell outcomes — using statistical models.


In many cases, several gene actions are triggered by the same factor. Even then, the statistical method can successfully determine what action resulted from what gene. Fluorescent markers indicate all gene activities, and the resulting mosaic can be statistically 'read' to spot exactly when a cell responds to a particular factor.

The method is so sensitive that it even shows when one deviation occurs in 40 otherwise identical cells. The fact that this one deviation actually is an effect and not a random occurrence, can be proven experimentally.


Study results are published in the journal the Proceedings of The National Academy of Sciences or PNAS.

Significance
Cell-to-cell variations in gene regulation occur in a number of biological contexts, such as development and cancer. Discovering regulatory heterogeneities in an unbiased manner is difficult owing to the population averaging that is required for most global molecular methods. Here, we show that we can infer single-cell regulatory states by mathematically deconvolving global measurements taken as averages from small groups of cells. This averaging-and-deconvolution approach allows us to quantify single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques. Our method is particularly relevant to solid tissues, where single-cell dissociation and molecular profiling is especially problematic.

Abstract
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.

Publication:
Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles
Fluorescence- in-situ-hybridization shows mRNA-activity in a tissue sample. Blue: low, red: high activity – Image: S. S. Bajikar / University of Virginia, Charlottesville (USA)

Authors:
Sameer S. Bajikar, Christiane Fuchs, Andreas Roller, Fabian J. Theis, and Kevin A. Janes

PNAS, Early Edition, 21 Januar 2014, Doi: 10.1073/pnas.1311647111

This work has been funded by the American Cancer Society, the National Institutes of Health, the German Research Foundation (DFG), the German Academic Exchange Service (DAAD), the Pew Scholars Program in the Biomedical Sciences, the David and Lucile Packard Foundation, the National Science Foundation and the European Research Council.