2.1: Using micropipettes correctly - Biology

2.1: Using micropipettes correctly - Biology

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Arguably, the most important scientific equipment that you will use in this class are adjustable micropipettes, which you will use in nearly every experiment. Micropipettes are precision instruments that are designed to accurately and precisely transfer volumes in the microliter range. Recall the relationships between volume units:

Accuracy and precision

Accuracy depends on the micropipette delivering the correct volume. Precise results
are reproducible. Let’s use a target analogy to demonstrate the difference between accurate and precise results. Imagine that four students try to hit the bulls-eye five times. Students A and B are precise, while students A and C are accurate.

Manufacturers determine the accuracy and precision of micropipettes by using them to transfer defined volumes of distilled water to a drop that is then weighed on an analytical balance. The density of water is 1.0 gram per mL at 25 ̊C. The process is repeated several times during the calibration process, and the data is used to calculate the accuracy and precision of a micropipette.

Accuracy refers to the performance of the micropipette relative to a standard (the intended) value. Accuracy is computed from the difference between the actual volume dispensed by the micropipette and the intended volume. Note that this can be a negative or positive
value. When micropipettes are calibrated, the accuracy is normally expressed as a percent of
the selected value. Micropipettes are designed to operate with accuracies within a few percent (generally <3%) of the intended value. The accuracy of a micropipette decreases somewhat when micropipettes are set to deliver volumes close to the lowest values in their range.

Precision provides information about reproducibility, without any reference to a standard. Precision reflects random errors that can never be entirely eliminated from a procedure. Thus, a series of repeated measurements should generate a normal or binomial distribution (opposite). Precision is expressed as the standard deviation (s) of the set of measurements. In a normal distribution, ~2/3 of measurements will fall within one standard deviation of the average or mean (x), and 95% of measurements will fall within two standard deviations of the mean. The standard deviation for a set of n measurements is calculated using the formula below.

Choosing the micropipette

Standard deviation describes the distribution of measurments relative to the mean value

We use three different sizes of micropipettes in the laboratory, the P20, P200 and P1000. Our micropipettes have been purchased from several different manufacturers, but the principles of operation are the same. The numbers after the “P” refer to the maximum number of microli- ters that the micropipette is designed to transfer. Note that there is some overlap in the ranges of the different micropipettes. For example, both the P200 and P20 can be used to transfer 15 μl, but the P20 is more accurate within that range. As a rule of thumb, always select the smallest volume pipette that will transfer the volume.

Specifying the transfer volume

There are three numbers on the volume indicator. With each of the micropipettes, you will specify a volume to three digits by turning the volume adjustment knob. You will also be able to extrapolate between the lowest numbers with the vernier marks on the lower dial. Most of the measurements you will make with the micropipettes will be accurate to four significant figures!


NEVER turn the indicator dial beyond the upper or lower volume limits of the micropipette! This could damage the piston.

​​​​Transferring volumes accurately

Micropipettes work by air displacement. The operator depresses a plunger that moves an internal piston to one of two different positions. The first stop is used to fill the micropipette tip, and the second stop is used to dispense the contents of the tip. As the operator depresses the plunger to the first stop, an internal piston displaces a volume of air equal to the volume shown on the volume indicator dial. The second stop is used only to dispense the contents of the tip.

Filling the micropipette

  • Remove the lid from the box containing the correct size micropipette tips. P-1000 tips may be blue or clear, while P-20 and P-200 tips are yellow or clear.
  • Attach the tip by inserting the shaft of the micropipette into the tip and pressing down firmly (figure on right). This should produce an airtight seal between the tip and the shaft of the micropipette.
  • Replace the lid of the tip box to keep the remaining tips sterile. Avoid touching the tip (especially the thinner end), because the tips are sterile.
  • Depress the plunger of the micropipette to the FIRST stop.
  • Immerse the tip a few millimeters below the surface of the solution being drawn up into the

    pipette. Pipetting is most accurate when the pipette is held vertically. Keep the angle less

    than 20 ̊ from vertical for best results.

  • Release the plunger S L O W L Y, allowing the tip to fill smoothly. Pause briefly to ensure

    that the full volume of sample has entered the tip. Do NOT let the plunger snap up. This is particularly important when transferring larger volumes, because a splash could contami- nate the shaft of the micropipette. If you inadvertently contaminate the shaft, clean it imme- diately with a damp Kimwipe.


NEVER rest a micropipette with fluid in its tip on the bench!

Dispensing the contents of the micropipette

  • Place the micropipette tip against the side of the receiving test tube. Surface tension will help to dispense the contents of the micropipette. Do NOT attempt to eject the contents of the micropipette into “thin air.”
  • Smoothly depress the plunger to the first stop. Pause, then depress the plunger to the second stop. The contents of the pipette should have been largely released at the first stop. The second stop ensures that you’ve released the “last drop.”
  • Use the tip ejector to discard the tip.


Adapting laboratory techniques for remote instruction

Pablo Perez-Pinera, left, and Karin Jensen developed remote laboratory exercises to help students learn common lab techniques. Credit: Karin Jensen

The COVID-19 pandemic forced instructors to adapt their courses for online learning. Laboratory courses were particularly difficult due to lack of access to specialized equipment for remote learners. To overcome this challenge, researchers from the University of Illinois Urbana-Champaign designed a laboratory exercise to teach students how to use micropipettes, through remote learning, using at-home kits.

Micropipettes are common—and essential—laboratory instruments and are used in several fields including molecular biology, microbiology, and biochemistry. They are used to accurately transfer very small volumes of liquid. To teach students how to use these instruments, the researchers developed kits, costing $135 per student, that were a fraction of the cost of the instructional equipment that is normally used for in-person classes.

"Although lab kits have been developed previously, they did not focus on micropipetting skills," said Karin Jensen, Teaching Assistant Professor of Bioengineering, who worked with Associate Professor of Bioengineering Pablo Perez-Pinera (ACPP) to develop the project. "In an effort to provide remote students with lab experience, we developed and shipped kits to students. These kits contained equipment and reagents for them to practice their technique and perform experiments remotely."

Each kit contained a mini-scale, a glucose meter, a pipet-aid, and a set of micropipettes. Each student was provided with the kit, an instructional video, and a laboratory manual. They were instructed to follow the protocol step by step with the goal of learning how to correctly dilute the glucose solutions and verifying their accuracy using the glucose meter. They also shared their data with the instructors for feedback and grading.

The students also filled out surveys and course feedback forms about the effectiveness of these online classes. "We found that most of the students were excited to use lab equipment despite being in an online section," Jensen said.

The researchers are now working to improve the exercise. "Beyond COVID-19, there is still a need to develop remote lab learning opportunities for students who cannot attend in-person labs," Jensen said. "Remote lab activities, similar to what we describe, will be important in increasing access to STEM education."

2.1: Using micropipettes correctly - Biology

In this lab, we learned how to use the micropipettes and the technique of gel electrophoresis correctly. In this, we could explain the importance of both these tools and what they do. Lastly, we learned how genetic engineering can be used to treat some diseases.

  1. It is important to use small and exact values because then you can make sure you know exactly what you are putting in and be more precise. This is good because what you are working with in genetic engineering is microscopic.

  1. It is important to see the solution enter and leave the tip because then you know that you are for sure transferring the solution.
  2. Precautions with the pipette tip are important because your body’s bacteria could potentially de-serilize the tip and alter your results.
  1. It is important to use gel electrophoresis because the larger and smaller strands of DNA will separate through the process, making the DNA easier to manipulate.

1. They must have a negative charge because they are attracted to the positive charges.

Single Step Lipid Extraction From Food Stuffs

Total lipid extraction from biological samples is a commonly used technique to assess the individual lipids that make up a tissue. One of the best described methodology for lipid extractions remains the Folch Extraction, named for the protocol’s author, Jordi Folch, who outlined this procedure in a seminal paper published in 1957. It very quickly became one of the most widely used protocols of its time, and is still the gold standard against which all other lipid extraction protocols are judged.

The premise of this technique is to first suspend homogenized tissues in a single phase (monophasic system) by the addition of chloroform:methanol mixture (2:1, v/v). This solvent mixture is chosen because it is moderately polar (see Polar Index below), which is helpful since the cells that make up the tissue are mostly made up of water — you want to be able to maximize how much of the polar and nonpolar components that can be dissolved (aka perturbation of intermolecular forces).

At this point in the extraction, the lipid sample is not pure. Many of the lipids are still strongly associated with non-lipid components such as proteins and carbohydrates (polysaccharides), reflecting their biological roles in cells. These “contaminants” can be removed by the addition of an aqueous solution (NaCl or KCl, for example), which helps the mixture to partition into 2 phases (biphasic system). The lipids are drawn into the chloroform portion, which is the least polar and most dense, and represents approximately 60% of the total volume. The upper phase, which is mostly methanol and water, contains mostly the non-lipid contaminants (though a residual amount of the more polar lipids might be present).

In this protocol, we have adapted the Folch Extraction to isolate lipids from common lipid-rich foods: avocados, eggs, and mayonnaise. These lipids can then be separated by thin layer chromatography, giving us insights into the lipids found in each ingredient.

Reaction Component Density Polarity Index*
Chloroform 1.49 g/cm 3 4.1
Methanol 0.79 g/cm 3 5.1
0.73% NaCl (H2 O) 1.01. g/cm 3 10.2

*The Polarity Index is a measurement of how reactive the solvent is with various polar test solutes. The higher the polarity index, the more polar the solvent.

2 Answers 2

This is wokring for EF Core 2.1 but if you're using EF Core 3.0 or higher versions please refer to this complete answer.

I can see that the extension SqlQuery was changed to FromSql

But the new FromSql method is more restrcitive than SqlQuery . The documentation of that method explains that it exists some limitations like:

SQL queries can only be used to return entity types that are part of your model. There is an enhancement on our backlog to enable returning ad-hoc types from raw SQL queries.
The SQL query must return data for all properties of the entity or query type.

[. ]
Update: more recent GitHub dotnet/efcore discussion Support raw SQL queries without defining an entity type for the result #10753

So in your case the SQL query you're using is the following:

As the documentation said you can only use FromSql with entity or query type. Your SQL query doesn't return all data of your entity defined in your model but it only returns one column of your entity. By the way a new feature is introduced in EF Core 2.1 which is in Release Candidate since 7 may 2018. Microsoft says:

EF Core 2.1 RC1 is a “go live” release, which means once you test that your application works correctly with RC1, you can use it in production and obtain support from Microsoft, but you should still update to the final stable release once it’s available.

##Using FromSql on query type##

An EF Core model can now include query types. Unlike entity types, query types do not have keys defined on them and cannot be inserted, deleted or updated (i.e. they are read-only), but they can be returned directly by queries. Some of the usage scenarios for query types are: mapping to views without primary keys, mapping to tables without primary keys, mapping to queries defined in the model, serving as the return type for FromSql() queries

If you want to use query type feature with your SQL text you first define a class, let's name it MySuperClass :

Then in your DbContext class defined a property of type DbQuery<MySuperClass> like below:

Finally you can use FromSql on it like below:

##Don't want to use DbQuery<T> ## If you don't want to use DbQuery<T> and don't want to define a class that contains only one property then you can use ExecuteSqlCommandAsync like @vivek nuna did in his answer(his answer is partially correct). But you must know that returned value by that method is the number of rows affected by your query. Also you must put your title as an output parameter so make your query a stored procedure. Use ExecuteSqlCommandAsync or ExecuteSqlCommand and after that read the output parameter you passed when calling the method.

A simpler way without creating a stored procedure therefore not using ExecuteSqlCommandAsync or ExecuteSqlCommand is to the following code:


We prove that each element is counted once.

Say that some element is in sets. Without loss of generality, these sets are

We proceed by induction. This is obvious for

If this is true for we prove this is true for For every set of sets not containing with size there is a set of sets containing with size In PIE, the sum of how many times these sets are counted is There is also one additional set of sets so is counted exactly once.

Welcome to the Goldberg Lab

Since 1973, Goldberg laboratory has been investigating the molecular processes controlling the development of specialized cells in higher plants. Our long-term goal is to understand the genes and regulatory networks required to make a seed. Our research projects are supported by the National Science Foundation (NSF) Plant Genome Program.

The major questions our research addresses are (1) how are genes organized in the genome, (2) what are the mechanisms that control the regulation of plant gene expression, (3) what are the sequences that program plant gene expression during development, (4) what are the genes that control the differentiation of specific plant cell types, and (5) what events cause an undifferentiated cell to take on a specialized state. We use a variety of genomic approaches and model plants to answer these questions &mdash with a particular focus on identifying and using the best suited approach for answering each specific question.

Professor Goldberg is wholeheartedly committed to teaching and public education. He created and currently teaches a novel course sponsored by the NSF that utilizes long-distance learning to teach students simultaneously at UCLA, UC Davis, and Tuskegee University.

Member of the National Academy of Sciences
Howard Hughes Medical Institute Professor
Founder of Plant Cell Journal

2.1: Using micropipettes correctly - Biology

Compiled binaries (for Mac, Windows and Linux) are available from the FigTree GitHub repository.


FigTree is designed as a graphical viewer of phylogenetic trees and as a program for producing publication-ready figures. As with most of my programs, it was written for my own needs so may not be as polished and feature-complete as a commercial program. In particular it is designed to display summarized and annotated trees produced by BEAST.

Compiled binaries (for Mac, Windows and Linux) are available from the FigTree GitHub repository.

Subscribe to the Figtree Announcement mailing list:
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Version History

Compiled binaries (for Mac, Windows and Linux) are available from the FigTree GitHub repository.

Author information


Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

Yoav Adam, Jeong J. Kim, Shan Lou, Michael E. Xie, Daan Brinks, Hao Wu, Simon Kheifets, Vicente Parot, Katherine J. Williams, Benjamin Gmeiner, Samouil L. Farhi & Adam E. Cohen

Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada

Yongxin Zhao & Robert E. Campbell

Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

Mohammed A. Mostajo-Radji & Paola Arlotta

Department of Neurobiology, Harvard Medical School, Boston, MA, USA

Selmaan Chettih & Christopher D. Harvey

Allen Institute for Brain Science, Seattle, WA, USA

Linda Madisen & Hongkui Zeng

Department of Statistics, Columbia University, New York, NY, USA

E. Kelly Buchanan, Ian Kinsella, Ding Zhou & Liam Paninski

Howard Hughes Medical Institute, Chevy Chase, MD, USA

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Y.A. performed the patch-clamp and imaging experiments in acute slices, cultured neurons and in vivo. J.J.K. performed patch-clamp measurements in HEK293T cells. Y.A. and Y.Z. performed protein engineering, with supervision by A.E.C. and R.E.C., respectively. Y.A. and D.B. performed spectroscopy experiments. Y.A. developed the imaging system with help from H.W., J.J.K., S.K. and V.P. S.L., L.M. and H.Z. developed and characterized the Ai155 Optopatch3 mice. M.A.M.-R. performed in utero electroporation, supervised by P.A. M.E.X. optimized and validated the penalized matrix decomposition–non-negative matrix factorization algorithm in collaboration with E.K.B., I.K., D.Z and L.P. S.C. and C.D.H. shared unpublished reagents for soma targeting of opsins. K.J.W. helped with molecular biology. B.G. performed the heating simulation. S.L.F. designed the CheRiff–HA construct. Y.A. and A.E.C. designed the project, analysed data and wrote the manuscript. A.E.C. supervised all aspects of the project.

Corresponding author

Supplementary Figure 1 Blebbing Walker cell migration is focal adhesion independent but depends on a rearward myosin contractility gradient.

a, Snapshot and kymograph of a blebbing Walker cell in microchannel (8 × 8 μm) coated with 50 μg ml −1 β-Lactoglobulin. Scale bars: horizontal: 10 μm, vertical: 50 s. b, Plot shows instantaneous velocity of Walker cells in microchannels coated with 50 μg ml −1 BSA or β-Lactoglobulin. P-value: Welch’s two-sided t-test, n: number of cells analysed in 2 (BSA) and 3 (β-Lactoglobulin) independent experiments. Boxes in boxplots extend from the 25th to 75th percentiles, with a line at the median. Whiskers extend to 1.5× IQR (interquartile range) or the max/min datapoints. c, Blebbing Walker cells expressing GFP-FAK do not form detectable focal adhesions during migration under agarose, while adherent Walker cells show focal adhesions during 2D migration on glass. Arrows indicate migration direction. Scale bars: 10 μm. d, Tracks of migrating blebbing Walker cells under agarose on glass, PEG-coated glass and commercially available low attachment surfaces. Cells were manually tracked for 52 min. Scale bars: 50 μm. e, Under agarose migration velocity of cells with reduced Talin expression levels (see also Supplementary Fig. 5). P-value: Welch’s two-sided t-test, n: number of cells analysed in 2 independent experiments. Boxes in boxplots extend from the 25th to 75th percentiles, with a line at the median. Whiskers extend to 1.5× IQR (interquartile range) or the max/min datapoints. f, Double laser ablation of the actomyosin cortex at the rear of the cell body (highest Myosin and F-Actin intensities) caused an instantaneous, strong decrease in cell velocity compared to a double ablation at the cell front. Scale bars: horizontal: 10 μm, vertical: 50 s. Plot shows reduction in cell velocity in % relative to the pre-ablation velocity. Horizontal bars: mean values. P-value: Welch’s two-sided t-test n: number of cells analysed in 3 (rear) and 2 (front) independent experiments. g, Confocal images of Walker cells labelled with MRLC-GFP: upper panel: cortex section, lower panel: middle cross section. Scale bar: 10 μm. The white rectangles highlight the ROI chosen for the analysis of myosin II intensity. h, To obtain cortical myosin intensity profiles (panel i), we subtracted the normalized fluorescence intensity profile in the middle cell section (cytoplasmic background, grey) from the myosin intensity profile acquired at the cell surface (black). n = 33 cells from 5 independent experiments. (i), Relative myosin fluorescence intensity profiles at the cortex for different friction conditions. n: number of cells analysed in 5 (BSA), 3 (BSA/F127) and 5 (F127) independent experiments All error bars: SEM

Supplementary Figure 2 Varying and measuring substrate friction.

a, Design of the microfluidic chip used for friction measurements. 3 entry ports were connected to reservoirs. Blowup shows region with bypass (h: 8 μm, w: 50 μm) and analysis channel (h: 8 μm, w: 8 μm). Micrograph shows single cell entering the analysis channel. Scale bar: 10 μm. b, Schematic of the measurement setup and principle of flow control. The pressure difference and thus the flow were controlled by adjusting the height of reservoir E3 relative to the other reservoirs E1 and E2. c, Calibration of the free average flow in the microfluidic friction device based on tracking of fluorescent beads. n: number of measurements pooled from several independent experiments as indicated in the figure. d, Maximum intensity projections of Z-stacks taken at the entry regions to microchannels coated with 50 μg ml −1 fluorescent BSA-488 mixed with 0 μg ml −1 , 30 μg ml −1 or 200 μg ml −1 F127. Scale bars: 10 μm e–g, Cell velocities and linear fits in microfluidic friction devices coated with F127, BSA-F127 mix or BSA. Error bars represent SEMs. For e n = 9 cells analysed from 3 independent experiments for f n = 10 cells analysed from 2 independent experiments for g n = 9 cells analysed from 3 independent experiments. h. Cell substrate friction coefficient measured for channels coated with F127, BSA-F127-mix and BSA. n: number of cells analysed from 3 (F127), 2 (BSA/F127) and 3 (BSA) independent experiments.

Supplementary Figure 3 Mechanical description of focal adhesion-independent migration.

a, Contact between a Walker cell and the channel wall visualized using interference reflection microscopy (IRM). Scale bar: 10 μm. b, Upper panel: Time-average (3 min) maximum intensity projection of the surface of a cell expressing MRLC-GFP. On timescales exceeding the bleb life cycle, the cell front has a near-hemispherical average shape. Scale bar: 10 μm. Lower panel: Parametrization and geometry of the axisymmetric cell surface. c, Coordinates used in different cell parts. d, Upper panel: Linear profile of myosin-generated active tension ζ(x) along the cell axis used for analytical calculations. Lower panel: Retrograde cortical flow profiles along the cell (reference frame of the cell). The cortical flow velocity depends on the friction coefficient. [vnorm = (ζ (r) − ζ (f) )L/η]. e, Cell velocity and external pressure. Upper panel: Schematic of internal and external pressures acting on the cell. Lower panel: Relation between cell velocity and external pressure difference shown for the parameter values in Table 1 (Supplementary Note). The stalling pressure of −15 Pa is three orders of magnitude lower than reported values for adhesive cells in micropipettes (around −2 kPa Supplementary Note Reference 17). f, Upper panel: Schematic of the frictional and drag forces acting on the cell in the absence of an external pressure difference. In the region where the cell contacts the channel wall, relative flow of the cortex to the wall generates frictional force. At the pole regions, fluid drag forces exerted by the surrounding medium oppose propulsive frictional forces. Lower panel: Cell velocity (solid lines) and maximum cortex velocity relative to the channel wall (dashed lines) as a function of the friction coefficient α and the drag coefficient αD. Cell motion occurs above a threshold friction α ∗ , set by the fluid drag coefficient: when friction is too small, cells cannot migrate against the drag force exerted by the fluid in the channel. g, Comparison between numerical evaluation of the inflexion point of the cell velocity U(α) (dots) and the proposed approximate relation α ∗ = D/(2L) (line), see Eq. 49. [αnorm = η/L 2 ]. Red dot: value obtained from the fitting procedure to experimental data.

Supplementary Figure 4 Role of the nucleus and internal friction (a–d), hydraulics of the channel-cell system (e), validation of the estimated fluid drag coefficient (f–h) and forces exerted on the channel walls (i).

(a), Quantification of nuclear cross-sectional area. Scale bar: 10 μm. b, c, Nuclear cross-sectional area does not correlate with cell velocity in intermediate (b) or high (c) friction channels. NExp = 1(b) or 4(c). (d), Schematic of internal friction arising from intracellular material. In this description, the intracellular pressure difference between the rear and front of the migrating cell is balanced by the frictional forces fint exerted by the actin cortex (see SI section 4.2). (e), A single cell migrating in a channel experiences drag forces governed by the fluid flow resistances in the channel. A channel segment containing a cell offers a flow resistance ξ while the fluid-filled channel is characterized by the resistance ξc. When multiple cells migrate in a channel, the fluid drag coefficient depends predominantly on the flow resistance of cell segments ξ. For definition of the fluid flow resistances of the rear and front part of the channel ξc (r) and ξc (f) see Equations 51-52. (f), Average fluid flow velocity induced by migrating cells was estimated by tracking microspheres in channels with rapidly migrating cells. Scale bar: 10 μm. (g), Kymograph of BSA-coated microchannel array with multiple migrating cells labelled with MRLC-GFP. Scale bars: horizontal: 500 μm, vertical: 30 min. (h), Histogram of cell velocities in BSA-coated channels obtained from kymographs of the full microchannel array. To allow for comparison with fluid flow measurements in channels with rapidly migrating cells, time-windows without cell velocities above 5.45 μm min −1 were discarded. The threshold was chosen according to the 0.1 quantile of the velocity distribution of polarised, fast cells used for cortical flow analysis. n = 164 cells from 2 independent experiments. (i), Distribution of forces exerted by migrating Walker cells on the channel wall in large, intermediate and small friction channels. Cell migration direction is to the right, the force is oriented on average in the direction opposite to cell motion, and the stress magnitudes are in the mPa-Pa range.