absorption microspectroscopy, theory and applications in the case of the photosynthetic compartment

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Micron 38 (2007) 197–213

Absorption microspectroscopy, theory and applications in

the case of the photosynthetic compartment

Laura Barsanti a, Valtere Evangelista a, Anna Maria Frassanito a,Nicoletta Vesentini b, Vincenzo Passarelli a, Paolo Gualtieri a,*

a Istituto di Biofisica, Area della Ricerca CNR, via Moruzzi 1, 56124 Pisa, Italyb Istituto di Fisiologia Clinica, Area della Ricerca CNR, via Moruzzi 1, 56124 Pisa, Italy

Abstract

We performed microspectroscopic evaluation of the pigment composition of the photosynthetic compartments of both algae and higher plants.

The feasibility of microspectroscopy for discriminating among species and/or phylogenetic groups was tested on laboratory cultures. Gaussian

bands decompositions, and a fitting algorithm, together with fourth-derivative transformation of absorbance spectra, provided a reliable

discrimination among chlorophylls, phycobiliproteins and carotenoids. Comparative analysis of absorption spectra highlighted the evolutionary

grouping of the algae into three main lineages in accordance with the most recent endosymbiotic theories.

# 2006 Elsevier Ltd. All rights reserved.

Keywords: Microspectroscopy; Algae; Pigment; Endosymbiosis; Gaussian decomposition; Fourth-derivative transformation

1. Introduction

Nature evolved a very limited number of chromophores in

the different evolutionary branches of the tree of life because of

the closely similar needs of organisms for the detection of the

external world and the interaction with it. Two cellular

compartments are the main location of endogenous chromo-

phores, the photoreceptors and the photosynthetic compart-

ments, i.e. the thylakoid membranes. The study of pigments and

their distribution in these cellular structures is usually based on

extractive procedures, followed by biochemical or spectro-

scopic assays. Extractive techniques often include disadvan-

tages: they can modify the nature of the components and they

are not successful in isolating the pigments. Direct spectro-

scopy on intact samples (microspectroscopy) has the advantage

of preserving the integrity of biological structures or

substructures during the measurement of absorption spectra;

if it is joined with successive mathematical computation, it can

discriminate between the different pigment contributions to the

spectral envelope.

In this review we will analyse the performance of

microspectroscopy, describe absorption spectra of thylakoid

compartments belonging to different algal specimens measured

* Corresponding author. Tel.: +39 050 3153026; fax: +39 050 3152760.

E-mail address: [email protected] (P. Gualtieri).

0968-4328/$ – see front matter # 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.micron.2006.07.015

under laboratory conditions and compare them with those

present in the literature, which have been recorded under

natural environmental conditions. Spectroscopic techniques

such as fluorescence or reflectance spectroscopy will be

reviewed successively.

Photosynthetic compartments belong to both algae and plants.

Absorption spectra measured in vivo on these compartments, can

give us very precise and accurate information about the spectral

range in which pigment molecules capture photons in their

natural environment. Therefore, the use of microspectrophoto-

metry can expand the perception of taxonomists, who identify

species in relation to natural pigmentation to supplement

classification based on morphology. Pigment distribution is

almost constant in plants, while it shows a high degree of

variation in algae. As consequence it is possible to predict the

presence of a specific pigment in an alga, to give an unknown alga

a plausible taxonomic framing, and to give support to the

phylogenetic tree of endosymbiotic events.

We will examine methods and theory behind a microspe-

trophotometer, and those that are used to identify the pigment

contributions in the absorption spectra of the photosynthetic

compartments of algae and plants.

We will review the following topics:

- B
iological background
- D
esign and performance of a microspectrophotometer
mailto:[email protected]

http://dx.doi.org/10.1016/j.micron.2006.07.015

L. Barsanti et al. / Micron 38 (2007) 197–213198

- T
ransfer of light energy through the optical system
- G
eometrical features of a microspectrophotometer
- T
heory of detection of photosensitive substances
- S
tatistics of photon flux and detection
- M
athematical computation of absorption spectra
- R
esults of microspectrophotometric investigations on photo-
synthetic compartments

- C
omparison of absorption spectra obtained by different
methods

- C
onclusions
2. Biological background

The term algae has no formal taxonomic standing,

nevertheless it is routinely used to indicate a polyphyletic,

non-cohesive and artificial assemblage, of O2-evolving,

photosynthetic organisms. No easily definable classification

system acceptable to all exists for algae, since taxonomy is

under constant and rapid revision at all levels following every

day new genetic and ultrastructural evidence. Keeping in mind

that the polyphyletic nature of the algal group is somewhat

inconsistent with traditional taxonomic groupings, though they

are still useful to define the general character and level of

organization, and aware of the fact that taxonomic opinion may

change as information accumulates, we have adopted a scheme

of classification mainly based on that of Van Den Hoek et al.

(1995). Prokaryotic members of this assemblage are grouped

into two divisions, i.e. Cyanophyta and Prochlorophyta, while

eukaryotic members are grouped into nine divisions, i.e.

Glaucophyta, Rhodophyta, Heterokontophyta, Haptophyta,

Cryptophyta, Dinophyta, Euglenophyta, Chlorarachniophyta

and Chlorophyta.

According to the most recent theories, different evolutionary

lineages can be recognized within the algal world. Three major

eukaryotic photosynthetic groups have descended from a

common prokaryotic ancestor through an endosymbiotic event.

The result is a set of nested cellular compartments one inside

the other and information about the evolutionary history of the

organism can be gleaned from the study of the membranes

surrounding these compartments, the genes that they express

and their function. The three lineages of primary plastids were

found in the Glaucophyta, in the green algae and plants, and in

the red algae. The other algal groups have acquired their

plastids via secondary (or tertiary) endosymbiosis, where a

eukaryote already equipped with plastids was preyed upon by a

second eukaryotic cell.

Photosynthetic compartments contain the pigments for

absorbing light and channeling the energy of the excited

pigment molecules into a series of photochemical and

enzymatic reactions. All those pigments are organized in

supra-molecular structures of pigment–protein complexes

embedded in the membrane of sac-like flat compressed

vesicles, the thylakoids. In prokaryotic algae the thylakoids

are free within the cytoplasm, while in eukaryotic algae they are

enclosed within bounding membranes to form the chloroplast.

The fact that algae of different divisions have different

colors, due to the presence of many different pigments in the

photosynthetic membrane system represents an important

diagnostic element at the taxonomic level of class. The pigments

present in algal cells (i.e. different type(s) of chlorophylls,

different type(s) of carotenoids and different type(s) of

phycobiliproteins) also provide a convenient paradigm to

explain evolutionary development involving endosymbiotic

acquisition of photosynthetic cellular organelles.

Algal pigmentations have taken two protective routes:

(1) U
V absorption by screening pigments or quenching of UV-
generated free radicals (mainly by carotenoids).

(2) A
voidance strategy entailing growth at very low light levels
in stratified habitats. This requires accessory pigments,

mainly phycobiliproteins and the other chlorophylls, to

harvest scarce photons in support of chlorophyll a that is

central to the terrestrial photosynthesis, i.e. the processes

and absorption of light at wavelengths other than those

absorbed by chlorophyll a and transfer of energy to the

reaction centers of the two photosystems. Both strategies

entail stratification of communities so that the spatial

distribution of pigments has a cumulative effect of

ecological significance.

3. Design and performance of a microspectrophotometer

A microspectrophotometer is a modified microscope that

can measure the absorption spectrum of a very small area inside

the cell. Basic set-ups can be described according to the way of

measuring transmitted light from sample and reference points

and wavelength control devices.

3.1. Single optics system with stage-object switching

In this system the geometry of both the photometric and the

illuminating beam must remain unchanged when specimen

and reference material are interchanged. For the interchange,

the specimen and the reference material must be bodily

moved, without modifying the set-up of the microscope

(Fig. 1).

This instrument has the following advantages: it can be

conveniently attached to all types of microscope; it has equal

train and transmittance in the light train for both specimen and

reference material, and it is not very costly (Gualtieri et al.,

1989).

3.2. Single optics system with beam switching

In this system the geometry of the photometric beam in

front of the photocathode remains almost unchanged when

the measuring spot in the image field or in the stage-object

field is switched from the specimen to the reference material,

but the direction of the photometric beam is changed, moving

either the condenser (Fig. 2) or the objective (Fig. 3). The

specimen and the reference material must be sufficiently

small for them to lie together in the full image field of the

microscope; the photometric beam is switched so that the

photocathode receives light first from the specimen and then

L. Barsanti et al. / Micron 38 (2007) 197–213 199

Fig. 1. Schematic diagram of a single optics system with stage-object switching:

(a) photomultiplier, (b) eyepiece, (c) objective, (d) stage-object and (e) condenser.

from the reference material. This type of photometric system

does not allow the luminous field to be adjusted individually

and properly with respect to the photometric field, hence it

allows glare. The optical trains for the measurement of the

Fig. 2. Schematic diagram of a single optics system with beam switching

obtained by moving the condenser: (a) photomultiplier, (b) eyepiece, (c)

objective, (d) stage-object and (e) condenser.

Fig. 3. Schematic diagram of a single optics system with beam switching

obtained by moving the objective: (a) photomultiplier, (b) eyepiece, (c)

objective, (d) stage-object and (e) condenser.

specimen and the reference material are not strictly equal,

and systematic errors may occur according to the cos4-law.

This law expresses the modification of the optical flux in an

oblique light tube with respect to that in a normal light tube.

The optical flux in the oblique tube is diminished by the

factor cos4 a, where a is the angle subtended between the

axis of the microscope and the axis of the oblique tube,

because light rays entering the microscope have its axis

oblique to the axis of the microscope, and not perpendicular

to the plane of the object. Notwithstanding, this system has

the advantage of high speed measurements (Benedetti et al.,

1976).

3.3. Double optics system

In this system, in addition to the optical train of the

microscope, there is a control train which supplies the radiant

flux needed as reference. The original optical train is split off

behind the condenser and is then recombined with the control

train immediately in front of the photocathode (Fig. 4). In spite

of the stringent condition about equality of optical path and

transmittance in the two trains, this system has the advantage of

high speed in the measurement and there is no restriction in size

and position of specimen or reference material (Liebman and

Entine, 1964).


Fig. 4. Schematic diagram of a double optics system: (a) photomultiplier, (b)

eyepiece, (c) mirror, (d) rotating mirror, (e) objective, (f) stage-object and (g)

condenser.

Fig. 5. Schematic diagram of a polychromator based instrumentation: (a)

grating polychromator, (b) CCD camera, (c) light-guide, (d) objective, (e)

stage-object and (f) condenser.

3.4. Reference-sample interchanging at each wavelength

This system requires the interchanging of the reference

material and the specimen in order to cover the entire visible

range (from 400 to 700 nm at 5 nm resolution); the main

drawback of this procedure is that after each repositioning the

photometric beam has to be positioned exactly in its previous

position on the specimen (Benedetti et al., 1976).

3.5. Wavelength change for sample and reference in

sequence

The interchanging of specimen and reference material is

made only once during the measurement. This involves changes

of the wavelengths from 400 to 700 nm for sample and

reference in sequence, and the procedure requires the stability

of both the lamp and the photoelectric (Gualtieri et al., 1989).

3.6. Polychromator based instrumentation

In this system a flat field imaging concave grating polychro-

mator is connected to a slit-shaped exit pupil of a light-guide

mounted onto the microscope. The polychromator dispersion

image is focused onto a digital slow scan cooled CCD camera

(Fig. 5). The images must be elaborated by specific software. The

main drawback of this procedure is the high cost of the apparatus,

which is however very fast.

3.7. A simple microspectrophotometer

A microspectrophotometer is a modified microscope with a

number of special sub-assemblies developed in house (Fig. 6).

The apparatus we used consists of a common microscope with

a 100 W tungsten-filament lamp (color temperature 3400 K)

and a stabilized 12 V/100 W power supply unit, equipped with

an holographic grating monochromator, two circular pinhole

diaphragms and a photomultiplier connected to a personal

computer. The wavelength is changed by a computer-

controlled stepping motor inserted on the axis of the

monochromator. One of the two diaphragms takes the place

of the field diaphragm, and the diameter of its image onto the

object plane is 8 mm. The second diaphragm is placed in the

plane of the real and inverted image, and its image onto the

object plane has a diameter of 0.5 mm. An oil-immersion

condenser (NA = 1.40) and a 100� oil-immersion planapo-

chromatic objective (NA = 1.25) are used for the measure-

ments. A photomultiplier is mounted on the binocular

phototube by means of a 40 mm diameter adapter ring,

beyond the measuring diaphragm and an eyepiece. The output

signal of the photomultiplier is amplified by a current amplifier

and transmitted to the AD card plugged into the computer bus.

The computer controls the position of the monochromator,


Fig. 6. Schematic diagram of a simple microspectrophotometer: (a) photo-

multiplier, (b) eyepiece, (c) diaphragm, (d) objective, (e) stage-object, (f)

condenser, (g) diaphragm, (h) holographic grating monochromator, (i) mono-

chromator slit and (j) lamp.

acquires the data, performs the calculation of optical density,

produces the graphic output and stores the data.

All the absorption spectra were recorded from 400 to

700 nm, with a step size of 0.5 nm and scan speed of

100 nm s�1. For each wavelength 10,000 values of optical

density were averaged. The resolution achieved with this step

size and wavelength width was sufficient to distinguish

between the major pigment classes present in the photosyn-

thetic compartments.

The first step of the procedure is the calibration. This is

performed by inserting a dark shutter in the optical path, and by

setting the dark-current reading of the current amplifier to zero.

A few drops of cell suspension are put onto a microscope slide

and Brownian movements are eliminated using 10% gelatine in

culture medium as a mounting medium since this solution gels

at room temperature and entraps the cells without causing

apparent changes in absorption. The specimen is also protected

from early evaporation by means of a gelatin edge-sealed

coverslip.

The absorbing structures are detected under microscope

examination, using a monochromatic light of wavelength

longer than 650 nm, or in phase contrast microscopy and the

selected structure is centred on the cross-hair of the finder.

The orientation of the structure with respect to the

electrical vector of light train has no practical importance,

except when we have to measure dichroic structures. In this

case, we can polarize the light beam, inserting a linear

polarizing filter just above the condenser. The 100� oil-

immersion planapochromatic objective (NA = 1.25) allows

the measurement of both the transmitted and the forward

scattered rays. These rays recombine in the plane of the real

image where the measuring diaphragm is located, and

therefore the scattering of the structure being measured does

not decrease the light impinging onto the photomultiplier.

Actually, the microspectrophotometer is and performs as a

confocal microscope.

Thereafter, the two diaphragms are aligned on an empty

bright field, maximizing the photocathode output value, and the

light transmitted for each wavelength is measured. Then, the

cell or the subcellular component on which the spectrum has to

be performed, is placed in the microscope field and finely

adjusted, and the light transmitted by the sample is measured.

The radiant flux impinging on the stage-object field is around

10�11 W for each measurement. This flux is regulated using the

optimal exposure value reported in the successive sections, in

order to equate the bleached pigment to the instrumental noise.

The value of the radiant flux corresponds to about 10�10 J of

energy striking the sample during the 5 s necessary to perform

the spectrum.

In the following we will describe the factors which influence

the light beam in the optical system. Knowledge of these factors

is required in order to enable the operator to control the beam in

such a way as to obtain the highest signal-to-noise ratio (SNR)

for photometric procedures (Piller, 1997).

4. Transfer of light energy in the optical system

The photoelectric sensor (phototube) transforms light

energy into an electric current. But our quantity, i.e. the

radiant flux, is really a power, i.e. energy per unit time. It is

symbolized by F and expressed in Watts (W).

Five factors determine the radiant flux impinging on the

surface of the photocathode:

(I) T
he initial power (IP) of the lamp, called spectral radiance
(W cm�2 sr�1 nm�1), that is the radiant flux per unit area,

unit solid angle and unit spectral bandwidth.

(II) T
he spectral bandwidth (SB) in nanometers (nm).
(III) T
he volume of the optical system (OF) through which the
beam passes, named optical flux or light tube by Zimmer

(1970). It is expressed as the ability of an optical system to

transfer light energy, but without considering loss of light

by absorption, reflection, scattering and diffraction. It is a

purely geometric quantity applied to the volume through

which the light is transferred and is expressed in cm2 sr.

The OF factor is

OF ¼ Fp NA2 (1)


where F is the area of aperture diaphragm (or of the image

of the diaphragm), and NA is the effective numerical

aperture.

The maximum optical flux in the microscope is

OF ¼ ðNAobj prÞ2 (2)

where r is the radius of the maximum field area at the

ocular level (Piller, 1997).The smallest optical flux giving

a reliable measuring value is about 10�8 cm2 sr. With a

circular pinhole of 0.5 mm in the object field, the value of

the effective numerical aperture has to be at least 1.0. With

a pinhole of 10 mm and an aperture of 0.1, we can obtain a

more favourable optical flux of 10�6 cm2 sr. In a normal

microscope the OF is about 10�3 cm2 sr.

(IV) T
he total transmittance (TR) of the optical system, which
is a measure of the fraction of light that remains after loss

by absorption (monochromatising device), reflection (lens

surfaces), scattering and diffraction.

(V) T
he interactions of the light with the sample (IF)
consisting in absorption, specular and diffuse reflection,

fluorescence, interference, refraction and diffraction;

these interactions obviously can influence each other.

The fundamental law of microspectrophotometry can be set out

as follows:

I ¼ IP � SB � OF � TR � IF (3)

As we have described before, measurements of absorption

microspectroscopy are based on the comparison of two radiant

fluxes density It and Ii. The first one It results from the

interaction of light with the specimen being measured (IF is

related to absorption cross-section of the molecules and the

number of absorbing molecules) (Cantor and Schimmel,

1980), and the second Ii results from the interaction of light

with the reference material. Therefore, we can consider the

absorbance of a specimen (Asp) as derived from the measures as

follows:

Asp ¼ logðIiÞ � logðItÞ (4)

This equation is known as the Lambert-Beer’s law, which is

valid for non-scattering substances, and for dilute solutions.

Knowing Asp, the path of light in the absorbing material, and the

extinction coefficient, we can calculate the concentration of a

chemical compound.

5. Geometrical features

In conventional microscopy, Abbe demonstrated how light

diffraction determines image resolution, and established the

role of numerical aperture (NA) of objective and condenser

lenses on image resolution (1/d), when the objective lenses used

are free from significant aberration, and when the field of view

is not extremely small (Born and Wolf, 1975).

d ¼ 1:22l

NAobj þ NAcond

(5)

The axial resolution, z, i.e. perpendicular to the plane of focus,

increases with the square of the NA and with the square of

lateral magnification. The distance from the centre of the

diffraction image to the first minimum along the microscope

axis turns out to be approximately twice the distance from the

centre of the diffraction image to the first minimum in the plane

of focus.

z ¼ 2l

n sin2ðaÞ(6)

The microspectroscope 3D response function constitutes an

optical probe with lateral (d) and axial (z) spatial dimension.

The illumination distribution and detection sensitivity distribu-

tion can be controlled by pinholes in the respective illumination

and detector paths. As indicated above, the optical probe for

confocal image formation is the convolution of both distribu-

tions.

The 3D shape of illuminating distribution may be controlled

by varying the effective NA of the used confocal lens. If the

illumination pinhole is set at a diameter di, the beam divergence

is equal to l/(2di). If the illumination lens has a (de)magnifica-

tion factor equal to M, Eqs. (5) and (6) can be written as

(Brakenhoff et al., 1979, 1989; Wilson, 1990):

d ¼ 1:2di

nM(7)

z ¼ 8d2i

nlM2(8)

Using the lens at its full maximum aperture u and in the case

that the detection pinhole diameter has intermediate values, the

effect of the spatial extension of the detection sensitivity

distribution depends on the detection pinhole diameter (dd) as:

d ¼ dd

M(9)

z ¼ 1:41dd

M tanðuÞ (10)

With very small detection pinholes the dimension of the

detection sensitivity function is determined by diffraction as

in Eqs. (7) and (8).

If the SNR is too low, some resolution can be sacrificed by

opening the detector pinhole to increase the signal. Then,

illumination distribution width can be increased to the detected

width, without additional loss of resolution. The probe intensity

is kept constant; hence the local irradiation load on the

specimen does not change. To maintain the statistical accuracy

of the measurements in the case the desired resolution doubles,

an irradiation dose four times greater for imaging a

bidimensional sample and a dose eight times greater in the

case of a three-dimensional sample will be necessary. Clearly

these conditions contain an inherent contradiction: in order to

achieve more quantitative temporal or spatial accuracy, more

light must pass through the sample but this will produce more

fading and cytotoxicity, which in turn will reduce the biological

reliability of the measurements. As the pinhole becomes


smaller, approaching a point detector, less light reaches the

detector, but transverse and axial resolution increase to

a maximum of 1.4 times the resolution of a wide-field

microscope.

Other microspectrophotometric instrumentations bridge

the gap between cells and molecules. At first glance, one

might wish to challenge this assertion by invoking the above

stated optical resolution limit imposed by the diffraction

phenomena. However, recent advancements in microscope

technology and data processing have extended the application

of microscopy to the study of dynamic properties of

molecules. The ‘‘ultramicroscopy effect’’ or ultraresolution

effect can be obtained by increasing the generalized Rayleigh

resolution limit without extending the spatial cutoff frequency

demanded by the diffraction (Cox and Sheppard, 1986). The

use of a very strong lateral illumination together with a dark

field condenser is the simplest way to obtain an ultraresolu-

tion effect (Passarelli et al., 1990). Moreover, by adding an

Epi-fluorescence illumination to the dark field illumination,

isolated objects such as colloidal particles as small as 20 nm

in diameter can be detected (Hirschberg et al., 1989). Among

the several possibilities, we want to emphasize the use of

Fluorescence Resonance Energy Transfer Microscopy (Jovin

and Arndt-Jovin, 1989), Fluorescence Photobleaching Recov-

ery Microscopy (Ware, 1989) and Differential Polarization

Imaging Microscopy (Finzi et al., 1989). These complex set-

ups prove advantageous because they incorporate both

sophisticated image-processing computers and confocal

microscope which greatly enhances the axial resolution of

the microscope for optical sectioning and true three-

dimensional imaging (Sheppard and Wilson, 1981). The

confocal microscope allows the selective excitation of only

that region of the specimen lying in the focal plane of the

objective, i.e. the specimen is not excited in its whole

thickness.

6. Theory of detection of photosensitive substances

In order to identify a substance by its light absorption

characteristics, we should be able to determine that the

difference between the average number of photon transmitted

by the sample and the average number of photon incident on the

sample (the signal) exceeds the random fluctuation (noise) by a

sufficient amount in the combined measuring and measured

system. The noise may arise from dark noise and photoelectron

shot noise in the photomultiplier (see the next section), from

amplifier or electrical circuit noise, from mechanical vibration,

and from biological motion due to Brownian movements or to

convection currents in the preparation. The last of these is easily

eliminated by adding gelatine to the culture medium for the

slide preparation; as already stated, the solution gels at room

temperature and entraps the cells without causing apparent

changes in the absorption properties of the cells. For the light

intensities used in Microspectrophotometry, the relative

contribution of dark current, i.e. the irreducible intrinsic limit

of any photoelectric measurement, is larger than that of other

possible noise sources.

The mean-square photoelectron noise or shot noise current,

In, at the level of the cathode of a photomultiplier is given by

(Liebman, 1972).

I2n ¼

e2kIiR

t(11)

where e is the charge of the electron, k the ratio of photoelec-

trons generated to photons incident at the cathode surface, Ii the

incident photon flux density at the object plane, R the area of

object illuminated and t is the detection time.

The signal to be detected is Is = Ii � It where Ii is the radiant

flux incident on the sample and It the flux density after

attenuation by the sample. Since the fraction absorbed by the

sample is A = 1 � T, where T is the fraction transmitted, it

follows that the signal is also Is = Ii � It = AIi.

This produces an average photoelectric signal current

difference Is, given by

Is ¼ ekAI iR (12)

The total mean-square shot noise involved in a measurement is

contributed essentially by the incident beam, if the absorption is

small (20% or less). We can calculate the root-mean-square of

signal-to-noise ratio of a shot-noise-limited photometer using

the determined values of both signal and noise. Therefore,

SNR ¼ Is

In

¼ AðkIitRÞ1=2(13)

Detectivity is quantitatively proportional to the signal-to-noise

ratio. The chances of detecting a substance that absorbs a

fraction A of the incident light are improved by selecting a

detector with a high quantum efficiency, k; and by using a high

flux density, Ii, and a long exposure time t; and by collecting

light from a larger area R, e.g. by using a larger sample. When

we attempt to apply these principles to the detection of

photoreceptor pigment in a single cell, however, several diffi-

culties arise. Both path-length and concentration of photore-

ceptor pigments in single cells are small and unalterable.

Again, if the light exposure E (Iit) is increased by increasing

either Ii or t, bleaching of the photoreceptor pigment could

occur. Moreover, the rate of pigment bleaching increases in

direct proportion to the exposure, whereas detectivity is

improved only as a square root of the exposure time. When

bleaching due to the measuring light alters a significant frac-

tion of the molecules originally present, distortion of the

recorded absorption spectrum may be expected and this can

occur in two ways. If the bleached molecules are quickly

replaced by product molecules that absorb little or no visible

light, the effect on the recorded curve will be to shift its

absorption maximum toward that end of the spectrum from

which the scan originates (Marks, 1965). If the bleached

molecules form colored photoproducts whose spectra differ

from those of the unbleached pigments, another kind of dis-

tortion occurs, which is more difficult to correct. Spectra of the

products as well as the kinetics constant of their decay are

required. Therefore, in order to obtain good spectral resolution

and detectivity for weakly-absorbing cells we need to use high

light exposure to reduce the relative noise fluctuations, but the


more light we use, the more pigment is bleached with increas-

ing danger of spectral distortion. Thus, a suitable compromise

between detectivity and bleaching should be found.

For small densities, the bleaching equation is (Liebman,

1972)

Pt

P0

¼ expð�IiteaÞ (14)

where P0 and Pt are respectively the amount (mol/cm2) of

pigment initially present and the amount present after a time, t,

of light exposure Ii that bleaches with an efficiency of e mmol of

pigment lost per mmol of photons absorbed; and a is the

naperian (logarithm to the base e) extinction coefficient.

At the end of the spectral scan, most of the pigment

originally present should be still present so that

Pt

P0

¼ expð�IiteaÞ � 1 (15)

This result is approached as exp(�Iitea) approaches zero.

The exponential function exp(�Iitea) can be expanded and

considered equal to (1 � Iitea) to a first approximation, that is

expð�IiteaÞ � 1� Iitea (16)

However, if Pb is the amount of pigment bleached, we can write

the following equation:

Pt þ Pb ¼ P0!Pt

P0

þ Pb

P0

¼ 1! Pb

P0

¼ 1� Pt

P0

(17)

By combining Eq. (17) with both Eqs. (15) and (16), we

have:

Pb

P0

¼ 1� Pt

P0

¼ Iitea (18)

When Pb is a small quantity, which is the experimentally

desirable condition, the approximation expressed in formula

(16), gives the exposure value (E = Iit) that must not be

exceeded for a given majority of pigment molecules to remain

unbleached.

Obviously, the optimum value of the exposure E is that

which equates the pigment bleached to the root-mean-square

photoelectron shot noise.

Thus, equating Eq. (13) with Eq. (18), and solving for the

exposure E we obtain this optimum value:

AðkIitRÞ1=2 ¼ P0

Pb

¼ ðIiteaÞ�1(19)

E ¼ ðA2a2e2kRÞ�1=3(20)

Therefore, E must be adjusted to be in inverse proportion to the

two-thirds power of the fraction A of the light absorbed by that

pigment. A pigment absorbing 10% of the incident light can

thus be recorded at a signal-to-noise ratio only 4.6 times higher

than that of a pigment absorbing 1% when the bleaching is

made equal to the noise.

7. Statistic of photon flux detection

With an ideal device, detection would be limited only by

the noise or background of the radiant source, and no

additional noise should be added by the detector itself or

subsequent amplification and conversion electronics. It is

possible to design ideal amplification and conversion stages,

but the detector scheme is still limited by the noise of the

detector stage itself. Internal detection with semiconductor

devices (photoconductive and photovoltaic) is associated with

two general classes of noise. The first class arises from

thermal motion of the charges within a material (Johnson

noise); the second class of noise is specific of the type of

device. In photoconductors, it is the generation–recombina-

tion noise, due to fluctuation in the rate of thermal generation

and recombination of charge carriers, which in turn generates

a fluctuation in the average carrier concentration, and

therefore a variation in average resistance. In diodes it is

the shot noise, due to the quantum nature of current carrier,

which generates a statistical variation of the amplitude of the

measured current. The final form of noise is the 1/f noise, so

named because the power varies more or less inversely with

frequency. In order to evaluate the SNR achievable by a

device, for example a photomultiplier, the behavior of such a

detector has to be analyzed in terms of SNR degradation

(Robben, 1971).

The detection process is limited by the SNR of the photon

flux. If the average rate of photon emission is Ip, then during the

observation interval t the average number of photons observed

is:

np ¼ Ipt (21)

the variance is given by

s2p ¼ Ipt ¼ np (22)

and the SNR is given by

SNR ¼ np

sp

¼ n1=2p (23)

For any photon incident on the photocathode, the quantum

efficiency, h, is the product of the probability that the photon is

absorbed and the probability that this photon produces a free

electron, and the probability that this free electron reaches the

surface of the material. Since the probability of releasing one

photoelectron per incident photon is related to the quantum

efficiency, we have

npe ¼ hIpt (24)

while the variance is given by

s2pe ¼ npe (25)

As a consequence, the SNR at the stage of emission of photo-

electrons from the photocathode is

SNRpe ¼ n1=2pe (26)


Fig. 7. Decomposition of chlorophyll a absorption spectrum obtained using

Gaussian (a), Lorenzian (b) and mixed Gaussian–Lorentians (c) profiles.

During any recording period, thermoionic electrons will be

emitted, and the average of these emissions and their variance

are related

s2pe ¼ nd (27)

Because the photoevent and the thermal event are independent,

the square values of their variance add,

s2pe ¼ nd þ npe (28)

and when dark noise is present, the SNR of a photomultiplier is

given by

SNRpe ¼npe

ðnpe þ ndÞ1=2(29)

8. Mathematical computation of absorption spectra

Absorbance attributable to accessory pigments is often

difficult to quantify. For this reason, the ability to discriminate

among distinct phylogenetic groups and potential species with

different thylakoidal pigment compositions will depend upon

the robustness of the technique chosen to differentiate the

diverse components within the portion of the spectrum where

carotenoids, phycobiliproteins and other chlorophylls absorb.

The relative contribution of the individual pigment classes to

absorption spectra can be estimated by curve-fitting routine

using templates of the absorption spectra of the pigments found

in the different divisions of algae, namely chlorophylls a, b and

c; cytochromes; carotenoids; and phycobiliproteins. Resolu-

tions of the different forms of chlorophyll c pigments that differ

only of 1 or 2 nm are not achievable with this analytical method.

The generation of templates of pigment absorption spectra is

based on the representation of the spectral dependence of the

pigment absorption in terms of a small number of symmetric

Gaussian bands (these bands are not attributable to a single

bond in the complex pigment molecules, but express the bulk

properties of the molecule) as in the following equation:

pigmentðlÞ ¼XL

j¼1

a j exp

�� ðl� l jÞ2

2s2j

�(30)

where aj is the specific absorption coefficient for pigment in the

jth Gaussian band, and s2j and lj are the widths and centers of

the corresponding band, and pigment(l) gives the absorption as

a function of wavelength.

The spectral decomposition could be equally performed

using Lorentzian or Voigt profiles (convolution of Lorentzian

and Gaussian bands).

Fig. 7a–c shows the decomposition of chlorophyll a

obtained using Gaussian, Lorenzian and mixed Gaussian–

Lorentians profiles; if the same number of bands and the same

position of the centers of the curves are used for each

decomposition, we can reconstruct the chlorophyll a spectra

without significative differences.

The number of Gaussian bands and their maxima can be

determined using the peaks of the fourth-derivative curve of

the absorption spectrum of extracted pigments. Fourth

derivative resolves the position of the absorbance maxima

attributable to pigment absorption bands (Bidigare et al., 1989;

Smith and Alberte, 1994). Fourth-derivative curve was adopted

because it shows maxima which could not be resolved by the

second-derivative curve. Since microspectroscopy has a high


signal-to-noise ratio the derivative operation does not produce

artefacts, i.e. the maxima resulting from derivation do

correspond to pigment absorption maxima. The widths of

the Gaussian bands, and their specific absorption coefficient aj

can be estimated on the basis of both the spectra of extracted

pigments and/or data present in the literature.

The Gaussian bands were fed into a spreadsheet. Band

parameters were varied automatically to find the smallest

difference between the pigment absorption obtained by Eq. (30)

[pigment(l)] and the absorbance of the pigment measured at

that wavelength; the resulting templates matched the absorption

recording without significant difference ( p < 0.01).

The pigment spectra templates can be then combined

together; the model that describes the spectrum for each

photosynthetic compartment is:

spectrumðlÞ ¼Xn

i¼1

Ci

XL

j¼1

ai j exp

�� ðl� li jÞ2

2s2i j

�(31)

where Ci is the concentration of the ith pigment, aij the specific

absorption coefficient for the ith pigment in the jth Gaussian

band, and lij are the widths of the corresponding band.

The centers s2i j of these Gaussian bands can be screened for

their correspondence with the peaks present in the fourth-

derivative curves of the absorption spectrum measured on the

photosynthetic compartments and shifted in the presumptive

real position, since the positions of the pigment absorption

bands in the proteic moieties are different from those in the

solvent solution.

The real centers of the bands of chlorophyll a (the most

important absorbing pigment) were determined using the

absorption spectrum of algae that contain only this chlorophyll

as main pigment. The peaks present in the red region and not

belonging to chlorophyll a were assigned to chlorophyll b or c

depending upon the algae where the determination was

straightforward.

The templates of the different pigment components were fed

into a spreadsheet, then summed and compared with the in vivo

absorption spectrum. Template concentrations were automa-

tically modulated to find the smallest difference between the

spectrum(l) and the in vivo absorbance at that wavelength. The

decomposition obtained by this procedure matched the in vivo

recording without significant difference ( p < 0.01).

9. Results of microspectrophotometric investigations on

photosynthetic compartments

In the following we will describe in detail the different

absorption spectra of the algae measured with the microspec-

trophotometric set-up described above.

Figs. 8–10 show the algae used for the measurements, the

color of each culture, the relative spectrum, and the main

pigments present in the cell and revealed by the decomposition

procedure.

The spectrum recorded on the trichomes of prokaryote

Leptolyngbya (Cyanophyta) shows that the main pigments of

this cyanobacterium are chlorophyll a, b-carotene, and the

phycobiliproteins phycoeritrocyanin, c-phycocyanin and allo-

phycocyanin (Fig. 8).

In the case of Prochlorotrix hollandica (Prochlorophyta), the

other prokaryotic alga, chlorophyll a is supported by

chlorophyll b, and the main carotenoid is zeaxanthin.

Phycobiliproteins are absent (Fig. 8).

The spectral patterns of both Cyanophora paradoxa

(Glaucophyta) (Fig. 8) and Porphyridium cruentum (Rhodo-

phyta) (Fig. 8) differ in that only chlorophyll a is present,

together with carotenoids, and phycobiliproteins. In the case of

Cyanophora, zeaxanthin is the main carotenoid, and the

phycobiliproteins are represented by c-phycocyanin and

allophycocyanin. In Porphyridium, lutein is the main carote-

noid, and r-phycocyanin, b-phycoerythrin and allophycocyanin

are the phycobiliproteins present.

In Ochromonas danica (Chrysophyceae) (Fig. 8) and

Phaeodactylum tricornutum (Bacillariophyceae) (Fig. 9) both

belonging to the division of Heterokontophyta, and in Pavlova

lutherii (Haptophyceae) (Fig. 9) belonging to the division of

Haptophyta, both chlorophylls a and c are present together with

fucoxanthin, which is the main carotenoid in all the three

species. In Nannochloropsis sp. (Eustigmatophyceae, Hetero-

kontophyta) (Fig. 9) only chlorophyll a and violaxantin are

present.

In Cryptomonas ovata (Cryptophyta) (Fig. 9) both

chlorophylls a and c are present, and the main carotenoid is

alloxanthin. Among the phycobiliproteins, phycoerithrin is the

only one detected, with the absorption maximum at 545 nm.

Phacus triqueter (Euglenophyta) (Fig. 9), Gymnochlora

stellata (Chlorarachniophyta) (Fig. 10) and Dunaliella salina

(Chlorophyta) (Fig. 10) share both chlorophylls a and b, and

differ in the main carotenoid, which is diadinoxanthin in

Phacus, neoxanthin in Gymnochlora and lutein in Dunaliella.

A very similar spectral pattern is present in the lawn daisy

Bellis perennis L., characterized by chlorophylls a and b, with

b-carotene as main carotenoid (Fig. 10).

The spectral patterns of three different dinoflagellates were

measured. Two of them share both chlorophylls a and c but

differ in the main carotenoid, which is peridinin in

Prorocentrum micans (Fig. 10), and fucoxanthin in the

undetermined sample (Fig. 10).

We measured the absorption spectra in algae cultivated

under laboratory conditions, since we considered the growth

conditions we adopted for our cultures as standard for

laboratory routines and small laboratory collections. Hence,

the pigment compositions we measured were interpreted as

typical for all members in individual algal classes, and the

identified pigments were taken as class-specific marker

pigments. Therefore, these pigment signatures can be used

as reference for identification purposes. In natural ecosystems,

factors such as light quality and quantity, nutrient availability,

salinity and temperature variations, and growth phase may

affect pigmentation dynamics. The total irradiance and

spectral quality of the available light undergo more or less

predictable variations; diurnal and seasonal cycles, atmo-

spheric and/or more localized shading phenomena can change

the flux of photosynthetically active radiation, while shading


Fig. 8. Results of microspectrophotometric measurement on photosynthetic compartments; each series shows the sample used, the color of the culture on filter, the

absorption spectrum, and the main pigments after spectrum band decomposition.



absorption spectrum, and the main pigments after spectrum band decomposition.



absorption spectrum, and the main pigments after spectrum band decomposition; in the case of the lawn daisy, the image of the plant substitutes for the image of the

filter showing the color of the culture.


Fig. 11. Comparison between absorption spectra obtained by different methods on diatoms belonging to different genera (a) and on green algae belonging to the same

genus (b); refer to the text for details. (€) Spectrophotometry on cell suspension on filter; (|) spectrophotometry on cell suspension in cuvette; (k)

microspectrophotometry on cells on slide; (^) microspectrophotometry on cells on filter.

by other algae, and selective absorption by various materials

suspended or dissolved in the water column can alter the

spectral distribution of the irradiance. Both these phenomena

are known to influence the photosynthetic machinery of algal

Fig. 12. Comparison between absorption spectra performed on algae belonging to dif

and Reynaud, 1977).

cells (Cunningham et al., 1989; Marquardt and Hanelt, 2004).

On the basis of these assumptions, now we will compare the

other spectra measured in the natural external conditions or

with other microspectrophotometers.

ferent genera of cyanobacteria (a–c) and red algae (d) (Hewes et al., 1998; Roger


Fig. 13. Absorption spectra of algae grown under different environmental conditions and habitats, performed by different methods, and present in the literature;

reference is reported inside the frame (Hewes et al., 1998; Roger and Reynaud, 1977).


10. Comparison of microspectrophotometric data with

other spectroscopic investigations on photosynthetic

compartments

In the following we will describe the different absorption

spectra of the algal measured in different environmental

conditions and with different spectrophotometric instruments

as they are reported in the literature.

The spectra were recorded using commercial spectro-

photometers very often equipped with integrating sphere on cell

suspension in cuvettes (Canto de Loura et al., 1987; Enriquez

et al., 2005; Gerloff-Elias et al., 2005; http://www.ioccg.org/

. . .; Janssen et al., 2001; Johnsen et al., 1994, 1999; Razi Naqvi

et al., 2004; Yoshii et al., 2002) on suspension filtered onto glass

fibre filters (Astoreca et al., 2005; http://www.ioccg.org/. . .;Lutz et al., 2001; Millie et al., 2002; Payri et al., 2001; Stæhr

and Cullen, 2003; Ting et al., 2002), on cell suspension dried to

give films of 0.5 mm (Erokhina et al., 2002), or using different

microspectrophotometer set-ups (Nultsch et al., 1983; Barsanti

et al., 1989, 1990; Graham, 2000; Albertano, 1991; Evangelista

et al., 2006).

Fig. 11a and b shows the comparison between different

methods. All the different experimental methods were tested on

diatoms (Bacillariophyceae) belonging to different genera

(Fig. 11a); though the spectra are comparable, still the

measurement performed by microspectrophotometry results

in a more resolved spectrum (in term of number of detected

pigment bands). Fig. 11b shows different methods applied to

the same species in the same environmental conditions;

therefore the differences between spectra must be due only

to the methods; also in this case the more resolved and detailed

spectrum has been obtained by microspectroscopy.

Fig. 12 shows spectra performed on algae belonging to

different genera of cyanobacteria (Fig. 12a–c) and red algae

(Fig. 12d), containing accessory pigments organized in

phycobilisomes. The spectra look very different, but the

difference between them is due to the environmental conditions

in which these algae dwell, supporting the hypothesis that

environmental parameters influence pigment distribution and

content.

Fig. 13 shows other absorption spectra present in the

literature, which have been performed by different methods, on

algae grown under different environmental conditions and

habitats. To extrapolate the specific experimental and environ-

mental parameters relative to each spectrum, the reader can

refer to corresponding paper cited under the references section.

11. Conclusions

Absorbance attributable to accessory photopigments often is

difficult to quantify and routinely discern for great number of

these. For that reason, the ability to discriminate among distinct

phylogenetic groups (and potential species) will be dependent

upon the effectiveness of the techniques chosen to finding out

‘‘real’’ Gaussian bands and their successive groupings.

Fourth-derivative spectra transformation of the microspec-

trophotometric absorbance spectra and the successive Gaussian

bands decomposition and their fitting into the spectral envelope

of pigment templates provides a realistic discrimination

between the different algal divisions.

For each division sensu Van Den Hoek et al. (1995) the

contribution and the spectral position of each pigment category

were well defined using the computation algorithms herein

described (see Section 8). Even taking into account the

inevitable approximations we made in the template reconstruc-

tions, the list of spectra presented here is the complete

description of pigment distribution in the thylakoid compart-

ments of the different algal divisions grown laboratory

condition (Figs. 8–10), and the microspectrophotometric

determination results the most detailed and accurate in terms

of detected pigments bands (Fig. 11a and b).

Therefore, these spectra can be used as a reference for the

comparison of the photosynthetic pigment present in the algae

growing under different conditions and diverse natural habitats,

which as shown in Figs. 12 and 13 clearly influence pigment

distributions and content.

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